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# AUTOMATED RELATIONAL META-LEARNING

**Anonymous authors**
Paper under double-blind review

ABSTRACT

In order to efficiently learn with small amount of data on new tasks, meta-learning
transfers knowledge learned from previous tasks to the new ones. However, a
critical challenge in meta-learning is the task heterogeneity which cannot be well
handled by traditional globally shared meta-learning methods. In addition, current
task-specific meta-learning methods may either suffer from hand-crafted structure
design or lack the capability to capture complex relations between tasks. In this
paper, motivated by the way of knowledge organization in knowledge bases, we
propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph.
When a new task arrives, it can quickly find the most relevant structure and tailor
the learned structure knowledge to the meta-learner. As a result, the proposed
framework not only addresses the challenge of task heterogeneity by a learned
meta-knowledge graph, but also increases the model interpretability. We conduct
extensive experiments on 2D toy regression and few-shot image classification and
the results demonstrate the superiority of ARML over state-of-the-art baselines.

1 INTRODUCTION

Learning quickly with a few samples is the key characteristic of human intelligence, which remains a
daunting problem in machine intelligence. The mechanism of learning to learn (a.k.a., meta-learning)
is widely used to generalize and transfer prior knowledge learned from previous tasks to improve
the effectiveness of learning on new tasks, which has benefited various applications, ranging from
computer vision (Kang et al., 2019; Liu et al., 2019) to natural language processing (Gu et al., 2018;
Lin et al., 2019). Most of existing meta-learning algorithms learn a globally shared meta-learner
(e.g., parameter initialization (Finn et al., 2017), meta-optimizer (Ravi & Larochelle, 2016), metric
space (Snell et al., 2017)). However, globally shared meta-learners fail to handle tasks lying in
different distributions, which is known as task heterogeneity. Task heterogeneity has been regarded as
one of the most challenging issues in few-shot learning, and thus it is desirable to design meta-learning
models that effectively optimize each of the heterogeneous tasks.

The key challenge to deal with task heterogeneity is how to customize globally shared meta-learner
by using task-aware information? Recently, a handful of works try to solve the problem by learning
a task-specific representation for tailoring the transferred knowledge to each task (Oreshkin et al.,
2018; Vuorio et al., 2019; Lee & Choi, 2018). However, the success of these methods relies on the
impaired knowledge generalization among closely correlated tasks (e.g., the tasks sampled from the
same distribution). Recently, learning the underlying structure among tasks provide a more effective
way for balancing the customization and generalization. Representatively, Yao et al. propose a
hierarchically structured meta-learning method to customize the globally shared knowledge to each
cluster in a hierarchical way (Yao et al., 2019). Nonetheless, the hierarchical clustering structure
completely relies on the handcrafted design which needs to be tuned carefully and may lack the
capability to capture complex relationships.

Hence, we are motivated to propose a framework to automatically extract underlying relational
structures from previously learned tasks and leverage those relational structures to facilitate knowledge
customization on a new task. This inspiration comes from the way of structuring knowledge in
knowledge bases (i.e., knowledge graphs). In knowledge bases, the underlying relational structures
across text entities are automatically constructed and applied to a new query to improve the searching
efficiency. In the meta-learning problem, similarly, we aim at automatically establishing the metaknowledge graph between prior knowledge learned from previous tasks. When a new task arrives,
it queries the meta-knowledge graph and quickly attends to the most relevant entities (nodes), and
then takes advantage of the relational knowledge structures between them to boost the learning
effectiveness with the limited training data.


-----

The proposed meta-learning framework is named as Automated Relational Meta-Learning (ARML).
Specifically, the ARML framework automatically builds the meta-knowledge graph from metatraining tasks to memorize and organize learned knowledge from historical tasks, where each vertex
represent one type of meta-knowledge (e.g., the common contour between birds and aircrafts). To
learn the meta-knowledge graph at meta-training time, for each task, we construct a prototype-based
relational graph for each class, where each vertex represents one prototype. The prototype-based
relational graph not only captures the underlying relationship behind samples, but alleviates the
potential effects of abnormal samples. The meta-knowledge graph is then learned by and summarizing
the information from the corresponding prototype-based relational graphs of meta-training tasks.
After constructing the meta-knowledge graph, when a new task comes in, the prototype-based
relational graph of the new task taps into the meta-knowledge graph for acquiring the most relevant
knowledge, which further enhances the task representation and facilitates its training process.

Our major contributions of the proposed ARML are three-fold: (1) it automatically constructs the
meta-knowledge graph to facilitate learning a new task; (2) it empirically outperforms state-of-the-art
meta-learning algorithms; (3) the meta-knowledge graph well captures the relationship among tasks
and improves the interpretability of meta-learning algorithms.

2 RELATED WORK

Meta-learning, allowing machines to learn new skills or adapt to new environments rapidly with a
few training examples, has been demonstrated to be successful in both supervised learning tasks
(e.g., few-shot image classification) and reinforcement learning settings. There are mainly three
research lines of meta-learning: (1) black-box amortized methods design black-box meta-learners
(e.g., neural networks) to infer the model parameters (Ravi & Larochelle, 2016; Andrychowicz et al.,
2016; Mishra et al., 2018); (2) gradient-based methods aim to learn an optimized initialization of
model parameters, which can be adapted to new tasks by a few steps of gradient descent (Finn et al.,
2017; 2018; Lee & Choi, 2018); (3) non-parameteric methods combine parameteric meta-learners
and non-parameteric learners to learn an appropriate distance metric for few-shot classification (Snell
et al., 2017; Vinyals et al., 2016; Yang et al., 2018; Oreshkin et al., 2018; Yoon et al., 2019).

Our work is built upon the gradient-based meta-learning methods. In the line of gradient-based
meta-learning, most algorithms learn a globally shared meta-learners from all previous tasks (Finn
et al., 2017; Li et al., 2017; Flennerhag et al., 2019), to improve the effectiveness of learning process
on new tasks. However, these algorithms typically lack the ability to handle heterogeneous tasks
(i.e., tasks sample from sufficient different distributions). To tackle this challenge, recent works
tailor the globally shared initialization to different tasks by leveraging task-specific information (Lee
& Choi, 2018; Vuorio et al., 2019; Oreshkin et al., 2018) and using probabilistic models (Grant
et al., 2018; Yoon et al., 2018; Gordon et al., 2019). Recently, HSML customizes the global shared
initialization with a manually designed hierarchical clustering structure to balance the generalization
and customization between previous tasks (Yao et al., 2019). However, the hierarchical structure
may not accurately reflect the real structure since it highly relies on the hand-crafted design. In
addition, the clustering structure further constricts the complexity of relational structures. However, to
customize each task, our proposed ARML leverages the most relevant structure from meta-knowledge
graph which are automatically constructed by previous knowledge. Thus, ARML not only discovers
more accurate underlying structures to improve the effectiveness of meta-learning algorithms, but
also the meta-knowledge graph can further enhance the model interpretability.

3 PRELIMINARIES

**Few-shot Learning** Considering a task Ti, the goal of few-shot learning is to learn a model with
a dataset Di = {Di[tr][,][ D]i[ts][}][, where the labeled training set][ D]i[tr] = {x[tr]j _[,][ y]j[tr][|∀][j][ ∈]_ [[1][, N][ tr][]][}][ only has a]
few samples and Di[ts] [represents the corresponding test set. A learning model (a.k.a., base model)][ f]
with parameters θ are used to evaluate the effectiveness on Di[ts] [by minimizing the expected empirical]
loss on Di[tr][, i.e.,][ L][(][D]T[tr]i _[, θ][)][, and obtain the optimal parameters][ θ][i][. For the regression problem, the loss]_
function is defined based on the mean square error (i.e., (xj _,yj_ )∈Di[tr] 2[) and for the clas-]

sification problem, the loss function uses the cross entropy loss (i.e., −[∥][f][P][θ][(]([x]x[j]j[)],y[−]j )[y]∈D[j][∥]i[tr][2] [log][ p][(][y][j][|][x][j][, f][θ][)][).]

Usually, optimizing and learning parameter θ for the task[P] _Ti with a few labeled training samples_
is difficult. To address this limitation, meta-learning provides us a new perspective to improve the
performance by leveraging knowledge from multiple tasks.


-----

**Meta-learning and Model-agnostic Meta-learning** In meta-learning, a sequence of tasks
_{T1, ..., TI_ _} are sampled from a task-level probability distribution p(T ), where each one is a few-shot_
learning task. To facilitate the adaption for incoming tasks, the meta-learning algorithm aims to find
a well-generalized meta-learner on I training tasks at meta-learning phase. At meta-testing phase, the
optimal meta-learner is applied to adapt the new tasks Tt. In this way, meta-learning algorithms are
capable of adapting to new tasks efficiently even with a shortage of training data for a new task.

Model-agnostic meta-learning (MAML) (Finn et al., 2017), one of the representative algorithms in
gradient-based meta-learning, regards the meta-learner as the initialization of parameter θ, i.e., θ0,
and learns a well-generalized initialization θ0[∗] [during the meta-training process. The optimization]
problem is formulated as (one gradient step as exemplary):


_θ0[∗]_ [:= arg min]
_θ0_


(fθi _,_ _i_ [) = arg min]
_L_ _D[ts]_ _θ0_
_i=1_

X


_L(fθ0−α∇θ_ _L(fθ_ _,Ditr_ [)][,][ D]i[ts][)][.] (1)
_i=1_

X


At the meta-testing phase, to obtain the adaptive parameter θt for each new task Tt, we finetune the
initialization of parameter θ0[∗] [by performing gradient updates a few steps, i.e.,][ f]θt [=][ f]θ0[∗] _t_ [)][.]

_[−][α][∇][θ]_ _[L][(][f][θ]_ _[,][D][tr]_

4 METHODOLOGY

In this section, we introduce the details of the proposed ARML. To better explain how it works,
we show its framework in Figure 1. The goal of ARML is to facilitate the learning process of new
tasks by leveraging transferable knowledge learned from historical tasks. To achieve this goal, we
introduce a meta-knowledge graph, which is automatically constructed at the meta-training time, to
organize and memorize historical learned knowledge. Given a task, which is built as a prototypebased relational structure, it taps into the meta-knowledge graph to acquire relevant knowledge for
enhancing its own representation. The enhanced prototype representation further aggregate and
incorporate with meta-learner for fast and effective adaptions by utilizing a modulating function. In
the following subsections, we elaborate three key components: prototype-based sample structuring,
automated meta-knowledge graph construction and utilization, and task-specific knowledge fusion
and adaptation, respectively.

**Propagation**

**Prototype-based** **Meta-knowledge**

**Prototypes** **Relational** **Graph )**

**Structure ℛ#**

+#(,

…
…
… !"

**Aggregator**

ℒ( **Modulation**

**Aggregator**

+#(- ℒ' ∇%ℒ !"#

!#


Figure 1: The framework of ARML. For each task _i, ARML first builds a prototype-based relational_
_T_
structure Ri by mapping the training samples Di[tr] [into prototypes, with each prototype represents]
one class. Then, Ri interacts with the meta-knowledge graph G to acquire the most relevant historical
knowledge by information propagation. Finally, the task-specific modulation tailors the globally
shared initialization θ0 by aggregating of raw prototypes and enriched prototypes, which absorbs
relevant historical information from the meta-knowledge graph.

4.1 PROTOTYPE-BASED SAMPLE STRUCTURING

Given a task which involves either classifications or regressions regarding a set of samples, we first
investigate the relationships among these samples. Such relationship is represented by a graph, called
prototype-based relational graph in this work, where the vertices in the graph denote the prototypes
of different classes while the edges and the corresponding edge weights are created based on the


-----

similarities between prototypes. Constructing the relational graph based on prototypes instead of raw
samples allows us to alleviate the issue raised by abnormal samples. As the abnormal samples, which
locate far away from normal samples, could pose significant concerns especially when only a limited
number of samples are available for training. Specifically, for classification problem, the prototype,
denoted by c[k]i

_[∈]_ [R][d][, is defined as:] _N_ _[tr]_


**c[k]i** [=]


_E(xj),_ (2)
_j=1_

X


_Nk[tr]_


where Nk[tr] [denotes the number of samples in class][ k][.][ E][ is an embedding function, which projects]
**xj into a hidden space where samples from the same class are located closer to each other while**
samples from different classes stay apart. For regression problem, it is not straightforward to construct
the prototypes explicitly based on class information. Therefore, we cluster samples by learning an
assignment matrix Pi R[K][×][N] _[tr]_ . Specifically, we formulate the process as:
_∈_

**Pi = Softmax(WpE** [T](X) + bp), c[k]i [=][ P]i[[][k][]][F] [(][X][)][,] (3)

where Pi[k] represents the k-th row of Pi. Thus, training samples are clustered to K clusters, which
serve as the representation of prototypes.

After calculating all prototype representations **c[k]i**
_{_ _[|∀][k][ ∈]_ [[1][, K][]][}][, which serve as the vertices in the the]
prototype-based relational graph Ri, we further define the edges and the corresponding edge weights.
The edge weight ARi (c[j]i _[,][ c]i[m][)][ between two prototypes][ c]i[j]_ [and][ c]i[m] [is gauged by the the similarity]
between them. Formally:

_ARi_ (c[j]i _[,][ c]i[m][) =][ σ][(][W]r[(][|][c][j]i_ _i_ _r[) +][ b]r[)][,]_ (4)

_[−]_ **[c][m][|][/γ]**

where Wr and br represents learnable parameters, γr is a scalar and σ is the Sigmoid function, which
normalizes the weight between 0 and 1. For simplicity, we denote the prototype-based relational graph
as Ri = (CRi _, ARi_ ), where CRi = {c[j]i _[|∀][j][ ∈]_ [[1][, K][]][} ∈] [R][K][×][d][ represent a set of vertices, with each]
one corresponds to the prototype from a class, while ARi = {|ARi (c[j]i _[,][ c]i[m][)][|∀][j, m][ ∈]_ [[1][, K][]][} ∈] [R][K][×][K]
gives the adjacency matrix, which indicates the proximity between prototypes.

4.2 AUTOMATED META-KNOWLEDGE GRAPH CONSTRUCTION AND UTILIZATION

In this section, we first discuss how to organize and distill knowledge from historical learning process
and then expound how to leverage such knowledge to benefit the training of new tasks. To organize
and distill knowledge from historical learning process, we construct and maintain a meta-knowledge
graph. The vertices represent different types of meta-knowledge (e.g., the common contour between
aircrafts and birds) and the edges are automatically constructed and reflect the relationship between
meta-knowledge. When serving a new task, we refer to the meta-knowledge, which allows us to
efficiently and automatically identify relational knowledge from previous tasks. In this way, the
training of a new task can benefit from related training experience and get optimized much faster
than otherwise possible. In this paper, the meta-knowledge graph is automatically constructed at the
meta-training phase. The details of the construction are elaborated as follows:

Assuming the representation of an vertex g is given by h[g] _∈_ R[d], we define the meta-knowledge
graph as G = (HG, AG), where HG = {h[j]|∀j ∈ [1, G]} ∈ R[G][×][d] and AG = {AG(h[j], h[m])|∀j, m ∈

[1, G]} ∈ R[G][×][G] denote the vertex feature matrix and vertex adjacency matrix, respectively. To better
explain the construction of the meta-knowledge graph, we first discuss the vertex representation H .
_G_
During meta-training, tasks arrive one after another in a sequence and their corresponding vertices
representations are expected to be updated dynamically in a timely manner. Therefore, the vertex
representation of meta-knowledge graph are defined to get parameterized and learned at the training
time. Moreover, to encourage the diversity of meta-knowledge encoded in the meta-knowledge graph,
the vertex representations are randomly initialized. Analogous to the definition of weight in the
prototype-based relational graph Ri in equation 4, the weight between a pair of vertices j and m is
constructed as:
_A_ (h[j], h[m]) = σ(Wo( **h[j]** **h[m]** _/γo) + bo),_ (5)
_G_ _|_ _−_ _|_
where Wo and bo represent learnable parameters and γo is a scalar.

To enhance the learning of new tasks with involvement of historical knowledge, we query the
prototype-based relational graph in the meta-knowledge graph to obtain the relevant knowledge in
history. The ideal query mechanism is expected to optimize both graph representations simultaneously


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at the meta-training time, with the training of one graph facilitating the training of the other. In light
of this, we construct a super-graph Si by connecting the prototype-based relational graph Ri with the
meta-knowledge graph G for each task Ti. The union of the vertices in Ri and G contributes to the
vertices in the super-graph. The edges in Ri and G are also reserved in the super-graph. We connect
_Ri with G by creating links between the prototype-based relational graph with the meta-knowledge_
graph. The link between prototype c[j]i [in prototype-based relational graph and vertex][ h][m][ in meta-]
knowledge graph is weighted by the similarity between them. More precisely, for each prototype c[j]i [,]
the link weight AS (c[j]i _[,][ h][m][)][ is calculated by applying softmax over Euclidean distances between][ c][j]i_
and {h[m]|∀m ∈ [1, G]} as follows:

_AS_ (c[j]i _[,][ h][k][) =]_ _Kexp(−∥(c[j]i_ _[−]_ **[h][k][)][/γ][s][∥]2[2][/][2)]** _,_ (6)
_k[′]_ =1 [exp(][−∥][(][c]i[j] _[−]_ **[h][k][′][ )][/γ][s][∥]2[2][/][2)]**

where γs is a scaling factor. We denote the intra-adjacent matrix asP **AS = {AS** (c[j]i _[,][ h][m][)][|∀][j][ ∈]_

[1, K], m ∈ [1, G]} ∈ R[K][×][G]. Thus, for task Ti, the adjacent matrix and feature matrix of super-graph
_i = (Ai, Hi) is defined as Ai = (A_ _i_ _, A_ ; A[T] [= (][C][R]i [;][ H][G][)][ ∈]
_S_ _R_ _S_ _S_ _[,][ A][G][)][ ∈]_ [R][(][K][+][G][)][×][(][K][+][G][)][ and][ H][i]
R[(][K][+][G][)][×][d], respectively.

After constructing the super-graph Si, we are able to propagate the most relevant knowledge from
meta-knowledge graph G to the prototype-based relational graph Ri by introducing a Graph Neural
Networks (GNN). In this work, following the “message-passing” framework (Gilmer et al., 2017),
the GNN is formulated as:
**Hi[(][l][+1)]** = MP(Ai, H[(]i[l][)][;][ W][(][l][)][)][,] (7)
where MP(·) is the message passing function and has several possible implementations (Hamilton
et al., 2017; Kipf & Welling, 2017; Velickoviˇ c et al., 2018),´ **H[(]i[l][)]** is the vertex embedding after l
layers of GNN and W[(][l][)] is a learnable weight matrix of layer l. The input H[(0)]i = Hi. After stacking
_L GNN layers, we get the information-propagated feature representation for the prototype-based_
relational graph Ri as the top-K rows of Hi[(][L][)], which is denoted as **C[ˆ]** _Ri = {cˆ[j]i_ _[|][j][ ∈]_ [[1][, K][]][}][.]

4.3 TASK-SPECIFIC KNOWLEDGE FUSION AND ADAPTATION

After propagating information form meta-knowledge graph to prototype-based relational graph, in
this section, we discuss how to learn a well-generalized meta-learner for fast and effective adaptions
to new tasks with limited training data. To tackle the challenge of task heterogeneity, in this
paper, we incorporate task-specific information to customize the globally shared meta-learner (e.g.,
initialization here) by leveraging a modulating function, which has been proven to be effective to
provide customized initialization in previous studies (Wang et al., 2019; Vuorio et al., 2019).

The modulating function relies on well-discriminated task representations, while it is difficult to learn
all representations by merely utilizing the loss signal derived from the test set Di[ts][. To encourage such]
stability, we introduce two reconstructions by utilizing two auto-encoders. There are two collections
of parameters, i.e, CRi and **C[ˆ]** _Ri, which contribute the most to the creation of the task-specific_
meta-learner. CRi express the raw prototype information without tapping into the meta-knowledge
graph, while **C[ˆ]** _Ri give the prototype representations after absorbing the relevant knowledge from the_
meta-knowledge graph. Therefore, the two reconstructions are built on CRi and **C[ˆ]** _Ri_ . To reconstruct
**CRi**, an aggregator AG[q](·) (e.g., recurrent network, fully connected layers) is involved to encode CRi
into a dense representation, which is further fed into a decoder AG[q]dec[(][·][)][ to achieve reconstructions.]
Then, the corresponded task representation qi of CRi is summarized by applying a mean pooling
operator over prototypes on the encoded dense representation. Formally,

_N_ _[tr]_


**qi = MeanPool(AG[q](CRi** )) =


(AG[q](c[j]i [))][,][ L][q][ =][ ∥][C][R]i _dec[(AG][q][(][C][R]i_ [))][∥]F[2] (8)
_j=1_ _[−]_ [AG][q]

X


_N_ _[tr]_


Similarly, we reconstruct **C[ˆ]** _Ri and get the corresponded task representation ti as follows:_

_N_ _[tr]_


**ti = MeanPool(AG[t]( C[ˆ]** _Ri_ )) =


_j=1(AG[t](ˆc[j]i_ [))][,][ L][t][ =][ ∥]C[ˆ] _Ri −_ AG[t]dec[(AG][t][( ˆ]CRi ))∥F[2] (9)

X


_N_ _[tr]_


The reconstruction errors in Equations 8 and 9 pose an extra constraint to enhance the training
stability, leading to improvement of task representation learning.


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**Algorithm 1 Meta-Training Process of ARML**

**Require: p(T ): distribution over tasks; K: Number of vertices in meta-knowledge graph; α: stepsize**
for gradient descent of each task (i.e., inner loop stepsize); β: stepsize for meta-optimization (i.e.,
outer loop stepsize); µ1, µ2: balancing factors in loss function

1: Randomly initialize all learnable parameters Φ
2: while not done do
3: Sample a batch of tasks {Ti|i ∈ [1, I]} from p(T )

4: **for all Ti do**

5: Sample training set Di[tr] [and testing set][ D]i[ts]

6: Construct the prototype-based relational graph Ri by computing prototype in equation 2
and weight in equation 4

7: Compute the similarity between each prototype and meta-knowledge vertex in equation 6
and construct the super-graph Si

8: Apply GNN on super-graph Si and get the information-propagated representation **C[ˆ]** _Ri_

9: Aggregate CRi in equation 8 and **C[ˆ]** _Ri in equation 9 to get the representations qi, ti and_
reconstruction loss Lq, Lt

10: Compute the task-specific initialization θ0i in equation 10 and update parameters θi =
_θ0i −_ _α∇θL(fθ, Di[tr][)]_

11: **end for**

12: Update Φ Φ _β_ Φ _Ii=1_ _i_ _[,][ D]i[ts][) +][ µ][i][L][t]_ [+][ µ][2][L][q]

13: end while _←_ _−_ _∇_ _[L][(][f][θ]_

P


After getting the task representation qi and ti, the modulating function is then used to tailor the
task-specific information to the globally shared initialization θ0, which is formulated as:

_θ0i = σ(Wg(ti ⊕_ **qi) + bg) ◦** _θ0,_ (10)

where Wg and bg is learnable parameters of a fully connected layer. Note that we adopt the Sigmoid
gating as exemplary and more discussion about different modulating functions can be found in
ablation studies of Section 5.

For each task Ti, we perform the gradient descent process from θ0i and reach its optimal parameter θi.
Combining the reconstruction loss Lt and Lq with the meta-learning loss defined in equation 1, the
overall objective function of ARML is:

_I_

minΦ Φ Φ _L(fθ0−α∇θ_ _L(fθ_ _,Ditr_ [)][,][ D]i[ts][) +][ µ]1[L]t [+][ µ]2[L]q[,] (11)

_[L][all][ = min]_ _[L][ +][ µ][1][L][t][ +][ µ][2][L][q][ = min]_ _i=1_

X

where µ1 and µ2 are introduced to balance the importance of these three items. Φ represents all
learnable parameters. The algorithm of meta-training process of ARML is shown in Alg. 2. The
details of the meta-testing process of ARML are available in Appendix A.

5 EXPERIMENTS

In this section, we conduct extensive experiments to demonstrate the effectiveness of the ARML on
2D regression and few-shot classification with the goal of answering the following questions: (1) Can
ARML outperform other meta-learning methods?; (2) Can our proposed components improve the
learning performance?; (3) Can ARML framework improve the model interpretability by discovering
reasonable meta-knowledge graph?

5.1 EXPERIMENTAL SETTINGS

**Methods for Comparison** We compare our proposed ARML with two types of baselines: gradientbased meta-learning algorithms and non-parameteric meta-learning algorithms.

_For gradient-based meta-learning methods: both globally shared methods (MAML (Finn et al.,_
2017), Meta-SGD (Li et al., 2017)) and task-specific methods (MT-Net (Lee & Choi, 2018), MUMOMAML (Vuorio et al., 2019), HSML (Yao et al., 2019)) are considered for comparison.

_For non-parametric meta-learning methods: we select globally shared method Prototypical Network_
(ProtoNet) (Snell et al., 2017) and task-specific method TADAM (Oreshkin et al., 2018) as baselines.
Note that, following the traditional settings, non-parametric baselines are only used in few-shot
classification problem. The detailed implementations of baselines are discussed in Appendix B.3.


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**Hyperparameter Settings** For the aggregated function in autoencoder structure (AG[t], AG[t]dec
AG[q], AG[q]dec[), we use the GRU as the encoder and decoder in this autoencoder framework. We]
adopt one layer GCN (Kipf & Welling, 2017) with tanh activation as the implementation of GNN
in equation 7. For the modulation network, we try both sigmoid, tanh Film modulation and find that
sigmoid modulation performs better. Thus, in the future experiment, we use the sigmoid modulation as
modulating function. More detailed discussion about experiment settings are presented in Appendix B.

5.2 2D REGRESSION

**Dataset Description.** In 2D regression problem, we adopt the similar regression problem settings
as (Finn et al., 2018; Vuorio et al., 2019; Yao et al., 2019; Rusu et al., 2019), which includes several
families of functions. In this paper, to model more complex relational structures, we design a 2D
regression problem rather than traditional 1D regression. Input x ∼ _U_ [0.0, 5.0] and y ∼ _U_ [0.0, 5.0]
are sampled randomly and random Gaussian noisy with standard deviation 0.3 is added to the
output. Furthermore, six underlying functions are selected, including (1) Sinusoids: z(x, y) =
_assin(wsx + bs), where as ∼_ _U_ [0.1, 5.0], bs ∼ _U_ [0, 2π] ws ∼ _U_ [0.8, 1.2]; (2) Line: z(x, y) = alx + bl,
where al ∼ _U_ [−3.0, 3.0], bl ∼ _U_ [−3.0, 3.0]; (3) Quadratic: z(x, y) = aqx[2] + bqx + cq, where aq ∼
_U_ [−0.2, 0.2], bq ∼ _U_ [−2.0, 2.0], cq ∼ _U_ [−3.0, 3.0]; (4) Cubic: z(x, y) = acx[3] + bcx[2] + ccx + dc,
where ac ∼ _U_ [−0.1, 0.1], bc ∼ _U_ [−0.2, 0.2], cc ∼ _U_ [−2.0, 2.0], dc ∼ _U_ [−3.0, 3.0]; (5) Quadratic
_Surface: z(x, y) = aqsx[2]_ + bqsy[2], where aqs ∼ _U_ [−1.0, 1.0], bqs ∼ _U_ [−1.0, 1.0]; (6) Ripple: z(x, y) =
_sin(−ar(x[2]_ + y[2])) + br, where ar ∼ _U_ [−0.2, 0.2], br ∼ _U_ [−3.0, 3.0]. Note that, function 1-4 are
located in the subspace of y = 1. Follow (Finn et al., 2017), we use two fully connected layers with
40 neurons as the base model. The number of vertices of meta-knowledge graph is set as 6.

**Results and Analysis.** In Figure 2, we summarize the interpretation of meta-knowledge graph
(see top figure) and the the qualitative results (see bottom table) of 10-shot 2D regression. In the
bottom table, we can observe that ARML achieves the best performance as compared to competitive
gradient-based meta-learning methods, i.e., globally shared models and task-specific models. This
finding demonstrates that the meta-knowledge graph is necessary to model and capture task-specific
information. The superior performance can also be interpreted in the top figure. In the left, we
show the heatmap between prototypes and meta-knowledge vertices (deeper color means higher
similarity). We can see that sinusoids and line activate V1 and V4, which may represent curve and
line, respectively. V1 and V4 also contribute to quadratic and quadratic surface, which also show
the similarity between these two families of functions. V3 is activated in P0 of all functions and the
quadratic surface and ripple further activate V1 in P0, which may show the different between 2D
functions and 3D functions (sinusoid, line, quadratic and cubic lie in the subspace). Specifically,
in the right figure, we illustrate the meta-knowledge graph, where we set a threshold to filter the
link with low similarity score and show the rest. We can see that V3 is the most popular vertice and

|Model|MAML Meta-SGD MT-Net MUMOMAML HSML ARML|
|---|---|


|10-shot|2.292 ± 0.163 2.908 ± 0.229 1.757 ± 0.120 0.523 ± 0.036 0.494 ± 0.038 0.438 ± 0.029|
|---|---|


connected with V1, V5 (represent curve) and V4 (represent line). V1 is further connected with V5,
demonstrating the similarity of curve representation.

V1

V2

Sinusoids Line

V0 V3

Quadratic Cubic

V5 V4

Quadratic Surface Ripple

Model MAML Meta-SGD MT-Net MUMOMAML HSML ARML

10-shot 2.292 0.163 2.908 0.229 1.757 0.120 0.523 0.036 0.494 0.038 **0.438** **0.029**


Figure 2: In the top figure, we show the interpretation of meta-knowledge graph. The left heatmap
shows the similarity between prototypes (P0, P1) and meta-knowledge vertices (V0-V5). The right
part show the meta-knowledge graph. In the bottom table, we show the overall performance (mean
square error with 95% confidence) of 10-shot 2D regression.


-----

5.3 FEW-SHOT CLASSIFICATION

**Dataset Description and Settings** In the few-shot classification problem, we first use the benchmark proposed in (Yao et al., 2019), where four fine-grained image classification datasets are included
(i.e., CUB-200-2011 (Bird), Describable Textures Dataset (Texture), FGVC of Aircraft (Aircraft),
and FGVCx-Fungi (Fungi)). For each few-shot classification task, it samples classes from one of four
datasets. In this paper, we call this dataset as Plain-Multi and each fine-grained dataset as subdataset.

Then, to demonstrate the effectiveness of our proposed model for handling more complex underlying
structures, in this paper, we increase the difficulty of few-shot classification problem by introducing
two image filters: blur filter and pencil filter. Similar as (Jerfel et al., 2019), for each image in PlainMulti, one artistic filters are applied to simulate a changing distribution of few-shot classification
tasks. After applying the filters, the total number of subdatasets is 12 and each tasks is sampled from
one of them. This data is named as Art-Multi. More detailed descriptions of the effect of different
filters is discussed in Appendix C.

Following the traditional meta-learning settings, all datasets are divided into meta-training, metavalidation and meta-testing classes. The traditional N-way K-shot settings are used to split training and
test set for each task. We adopt the standard four-block convolutional layers as the base learner (Finn
et al., 2017; Snell et al., 2017). The number of vertices of meta-knowledge graph for Plain-Multi
and Art-Multi datasets are set as 4 and 8, respectively. Additionally, for the miniImagenet, similar
as (Finn et al., 2018), which tasks are constructed from a single domain and do not have heterogeneity,
we compare our proposed ARML with other baselines and present the results in Appendix D.

5.3.1 PERFORMANCE VALIDATION

**Overall Qualitative Analyses** Experimental results for Plain-Multi and Art-Multi are shown in
Table 1 and Table 2, respectively. For each dataset, the performance accuracy with 95% confidence
interval are reported. Note that, due to the space limitation, in Art-Multi dataset, we only show
the average value of each filter and the full results table are shown in Table 9 of Appendix E. In
these two tables, first, we can observe that task-specific models (MT-Net, MUMOMAML, HSML,
TADAM) significantly outperforms globally shared models (MAML, Meta-SGD, ProtoNet) in both
gradient-based and non-parametric meta-learning research lines. Second, compared ARML with
other task-specific gradient-based meta-learning methods, the better performance confirms that
ARML can model and extract task-specific information more accurately by leveraging the constructed
meta-knowledge graph. Especially, the performance gap between the ARML and HSML verifies the
benefits of relational structure compared with isolated clustering structure. Finally, as a gradient-based
meta-learning algorithm, ARML can also outperform ProtoNet and TADAM, two representative
non-parametric meta-learning algorithms.

Table 1: Overall few-shot classification results (accuracy ± 95% confidence) on Plain-Multi dataset.

|Settings|Algorithms|Data: Bird Data: Texture Data: Aircraft Data: Fungi|
|---|---|---|

|MAML 53.94 ± 1.45% 31.66 ± 1.31% 51.37 ± 1.38% 42.12 ± 1.36% MetaSGD 55.58 ± 1.43% 32.38 ± 1.32% 52.99 ± 1.36% 41.74 ± 1.34% MT-Net 58.72 ± 1.43% 32.80 ± 1.35% 47.72 ± 1.46% 43.11 ± 1.42% 5-way MUMOMAML 56.82 ± 1.49% 33.81 ± 1.36% 53.14 ± 1.39% 42.22 ± 1.40% 1-shot HSML 60.98 ± 1.50% 35.01 ± 1.36% 57.38 ± 1.40% 44.02 ± 1.39% ProtoNet 54.11 ± 1.38% 32.52 ± 1.28% 50.63 ± 1.35% 41.05 ± 1.37% TADAM 56.58 ± 1.34% 33.34 ± 1.27% 53.24 ± 1.33% 43.06 ± 1.33% ARML 62.33 ± 1.47% 35.65 ± 1.40% 58.56 ± 1.41% 44.82 ± 1.38%|MAML MetaSGD MT-Net MUMOMAML HSML|53.94 ± 1.45% 31.66 ± 1.31% 51.37 ± 1.38% 42.12 ± 1.36% 55.58 ± 1.43% 32.38 ± 1.32% 52.99 ± 1.36% 41.74 ± 1.34% 58.72 ± 1.43% 32.80 ± 1.35% 47.72 ± 1.46% 43.11 ± 1.42% 56.82 ± 1.49% 33.81 ± 1.36% 53.14 ± 1.39% 42.22 ± 1.40% 60.98 ± 1.50% 35.01 ± 1.36% 57.38 ± 1.40% 44.02 ± 1.39%|
|---|---|---|
||ProtoNet TADAM|54.11 ± 1.38% 32.52 ± 1.28% 50.63 ± 1.35% 41.05 ± 1.37% 56.58 ± 1.34% 33.34 ± 1.27% 53.24 ± 1.33% 43.06 ± 1.33%|
||ARML|62.33 ± 1.47% 35.65 ± 1.40% 58.56 ± 1.41% 44.82 ± 1.38%|

|ARML 62.33 ± 1.47% 35.65 ± 1.40% 58.56 ± 1.41% 44.82 ± 1.38%|ARML|62.33 ± 1.47% 35.65 ± 1.40% 58.56 ± 1.41% 44.82 ± 1.38%|
|---|---|---|
|MAML 68.52 ± 0.79% 44.56 ± 0.68% 66.18 ± 0.71% 51.85 ± 0.85% MetaSGD 67.87 ± 0.74% 45.49 ± 0.68% 66.84 ± 0.70% 52.51 ± 0.81% MT-Net 69.22 ± 0.75% 46.57 ± 0.70% 63.03 ± 0.69% 53.49 ± 0.83% 5-way MUMOMAML 70.49 ± 0.76% 45.89 ± 0.69% 67.31 ± 0.68% 53.96 ± 0.82% 5-shot HSML 71.68 ± 0.73% 48.08 ± 0.69% 73.49 ± 0.68% 56.32 ± 0.80% ProtoNet 68.67 ± 0.72% 45.21 ± 0.67% 65.29 ± 0.68% 51.27 ± 0.81% TADAM 69.13 ± 0.75% 45.78 ± 0.65% 69.87 ± 0.66% 53.15 ± 0.82% ARML 73.34 ± 0.70% 49.67 ± 0.67% 74.88 ± 0.64% 57.55 ± 0.82%|MAML MetaSGD MT-Net MUMOMAML HSML|68.52 ± 0.79% 44.56 ± 0.68% 66.18 ± 0.71% 51.85 ± 0.85% 67.87 ± 0.74% 45.49 ± 0.68% 66.84 ± 0.70% 52.51 ± 0.81% 69.22 ± 0.75% 46.57 ± 0.70% 63.03 ± 0.69% 53.49 ± 0.83% 70.49 ± 0.76% 45.89 ± 0.69% 67.31 ± 0.68% 53.96 ± 0.82% 71.68 ± 0.73% 48.08 ± 0.69% 73.49 ± 0.68% 56.32 ± 0.80%|
||ProtoNet TADAM|68.67 ± 0.72% 45.21 ± 0.67% 65.29 ± 0.68% 51.27 ± 0.81% 69.13 ± 0.75% 45.78 ± 0.65% 69.87 ± 0.66% 53.15 ± 0.82%|
||ARML|73.34 ± 0.70% 49.67 ± 0.67% 74.88 ± 0.64% 57.55 ± 0.82%|


-----

Table 2: Overall few-shot classification results (accuracy ± 95% confidence) on Art-Multi dataset.

|Settings|Algorithms|Avg. Origninal Avg. Blur Avg. Pencil|
|---|---|---|


|MAML 42.70 ± 1.35% 40.53 ± 1.38% 36.71 ± 1.37% MetaSGD 44.21 ± 1.38% 42.36 ± 1.39% 37.21 ± 1.39% MT-Net 43.94 ± 1.40% 41.64 ± 1.37% 37.79 ± 1.38% 5-way, 1-shot MUMOMAML 45.63 ± 1.39% 41.59 ± 1.38% 39.24 ± 1.36% HSML 45.68 ± 1.37% 42.62 ± 1.38% 39.78 ± 1.36% Protonet 42.08 ± 1.34% 40.51 ± 1.37% 36.24 ± 1.35% TADAM 44.73 ± 1.33% 42.44 ± 1.35% 39.02 ± 1.34% ARML 47.92 ± 1.34% 44.43 ± 1.34% 41.44 ± 1.34%|MAML MetaSGD MT-Net MUMOMAML HSML|42.70 ± 1.35% 40.53 ± 1.38% 36.71 ± 1.37% 44.21 ± 1.38% 42.36 ± 1.39% 37.21 ± 1.39% 43.94 ± 1.40% 41.64 ± 1.37% 37.79 ± 1.38% 45.63 ± 1.39% 41.59 ± 1.38% 39.24 ± 1.36% 45.68 ± 1.37% 42.62 ± 1.38% 39.78 ± 1.36%|
|---|---|---|
||Protonet TADAM|42.08 ± 1.34% 40.51 ± 1.37% 36.24 ± 1.35% 44.73 ± 1.33% 42.44 ± 1.35% 39.02 ± 1.34%|
||ARML|47.92 ± 1.34% 44.43 ± 1.34% 41.44 ± 1.34%|


|ARML 47.92 ± 1.34% 44.43 ± 1.34% 41.44 ± 1.34%|ARML|47.92 ± 1.34% 44.43 ± 1.34% 41.44 ± 1.34%|
|---|---|---|
|MAML 58.30 ± 0.74% 55.71 ± 0.74% 49.59 ± 0.73% MetaSGD 57.82 ± 0.72% 55.54 ± 0.73% 50.24 ± 0.72% MT-Net 57.95 ± 0.74% 54.65 ± 0.73% 49.18 ± 0.73% 5-way, 5-shot MUMOMAML 58.60 ± 0.75% 56.29 ± 0.72% 51.15 ± 0.73% HSML 60.63 ± 0.73% 57.91 ± 0.72% 53.93 ± 0.72% Protonet 58.12 ± 0.74% 55.07 ± 0.73% 50.15 ± 0.74% TADAM 60.35 ± 0.72% 58.36 ± 0.73% 53.15 ± 0.74% ARML 61.78 ± 0.74% 58.73 ± 0.75% 55.27 ± 0.73%|MAML MetaSGD MT-Net MUMOMAML HSML|58.30 ± 0.74% 55.71 ± 0.74% 49.59 ± 0.73% 57.82 ± 0.72% 55.54 ± 0.73% 50.24 ± 0.72% 57.95 ± 0.74% 54.65 ± 0.73% 49.18 ± 0.73% 58.60 ± 0.75% 56.29 ± 0.72% 51.15 ± 0.73% 60.63 ± 0.73% 57.91 ± 0.72% 53.93 ± 0.72%|
||Protonet TADAM|58.12 ± 0.74% 55.07 ± 0.73% 50.15 ± 0.74% 60.35 ± 0.72% 58.36 ± 0.73% 53.15 ± 0.74%|
||ARML|61.78 ± 0.74% 58.73 ± 0.75% 55.27 ± 0.73%|



**Model Ablation Study** In this section, we perform the ablation study of the proposed ARML to
demonstrate the effectiveness of each component. The results of ablation study on 5-way, 5-shot
scenario for Art-Multi dataset are presented in Table 3. In Appendix F, we also show the full results
for Art-Multi in Table 6 and the ablation study of Plain-Multi in Table 7. Specifically, to show
the effectiveness of prototype construction, in ablation I, we use the mean pooling aggregation
of each sample rather than the prototype-based relational graph to interact with meta-knowledge
graph. In ablation II, we use all samples to construct the sample-level relational graph without
using the prototype. Compared with ablation I and II, the better performance of ARML shows
that structuring samples can (1) better handling the underlying relations (2) alleviating the effect of
potential anomalies by structuring samples as prototypes.

In ablation III, we remove the meta-knowledge graph and use the prototype-based relational graph
structure with aggregator AG[q] as the task representation. The better performance of ARML demonstrates the effectiveness of meta-knowledge graph for capturing the relational structure and facilitating
the classification performance. We further remove the reconstruction loss and show the results in
ablation IV and the results demonstrate that the autoencoder structure can benefit the process of
learning the representation.

In ablation VI and VII, we change the modulate function to film (Perez et al., 2018) and tanh,
respectively. We can see that ARML is not very sensitive to the modulating function, and sigmoid
function is slightly better than other activation functions in most cases.

Table 3: Results (accuracy ± 95% confidence) of Ablation Models (5-way, 5-shot) on Art-Multi.

|Ablation Models|Ave. Original Ave. Blur Ave. Pencil|
|---|---|

|I. no prototype-based graph II. no prototype|60.80 ± 0.74% 58.36 ± 0.73% 54.79 ± 0.73% 61.34 ± 0.73% 58.34 ± 0.74% 54.81 ± 0.73%|
|---|---|

|III. no meta-knowledge graph IV. no reconstruction loss|59.99 ± 0.75% 57.79 ± 0.73% 53.68 ± 0.74% 59.07 ± 0.73% 57.20 ± 0.74% 52.45 ± 0.73%|
|---|---|

|V. tanh modulation VI. film modulation|62.34 ± 0.74% 58.58 ± 0.75% 54.01 ± 0.74% 60.06 ± 0.75% 57.47 ± 0.73% 52.06 ± 0.74%|
|---|---|

|ARML|61.78 ± 0.74% 58.73 ± 0.75% 55.27 ± 0.73%|
|---|---|


5.3.2 ANALYSIS OF CONSTRUCTED META-KNOWLEDGE GRAPH

In this section, we conduct extensive analysis for the constructed meta-knowledge graph, which is
regarded as the key component in ARML. Due to the space limit, we only present the results on ArtMulti datasets. For Plain-Multi, the analysis with similar observations are discussed in Appendix G.


-----

**Performance v.s. Vertice Numbers** We first investigate the impact of vertice numbers in metaknowledge graph. The results are shown in Table 4. From the results, we can notice that the
performance saturates as the number of vertices researches around 8. One potential reason is that 8
vertices is enough to capture the potential relations. If we have a larger datasets with more complex
relations, more vertices may be needed. In addition, if the meta-knowledge graph do not have enough
vertices, the worse performance suggests that the graph may not be able to capture enough relations
across tasks.

Table 4: Sensitivity analysis with different # of vertices in meta-knowledge graph (5-way, 5-shot).

|# of vertices|Ave. Original Ave. Blur Ave. Pencil|
|---|---|


|4 8 12 16 20|61.18 ± 0.72% 58.13 ± 0.73% 54.88 ± 0.75% 61.78 ± 0.74% 58.73 ± 0.75% 55.27 ± 0.73% 61.66 ± 0.73% 58.61 ± 0.72% 55.07 ± 0.74% 61.75 ± 0.73% 58.67 ± 0.74% 55.26 ± 0.73% 61.91 ± 0.74% 58.92 ± 0.73% 55.24 ± 0.72%|
|---|---|



**Model Interpretation Analysis of Meta-Knowledge Graph** We then analyze the learned metaknowledge graph. For each subdataset, we randomly select one task as exemplary. For each task,
in the left part of Figure 3 we show the similarity heatmap between prototypes and vertices in
meta-knowledge graph, where deeper color means higher similarity. V0-V8 and P1-P5 denotes
the different vertices and prototypes, respectively. The meta-knowledge graph is also illustrated
in the right part. Similar as the graph in 2D regression, we set a threshold to filter links with low
similarity and illustrate the rest of them. First, We can see that the V1 is mainly activated by bird
and aircraft (including all filters), which may reflect the shape similarity between bird and aircraft.
Second, V2, V3, V4 are firstly activated by texture and they form a loop in the meta-knowledge
graph. Especially, V2 also benefits images with blur and pencil filters. Thus, V2 may represent the
main texture and facilitate the training process on other subdatasets. The meta-knowledge graph also
shows the importance of V2 since it is connected with almost all other vertices. Third, when we use
blur filter, in most cases (bird blur, texture blur, fungi blur), V7 is activated. Thus, V7 may show the
similarity of images with blur filter. In addition, the connection between V7 and V2 and V3 show that
classify blur images may depend on the texture information. Fourth, V6 (activated by aircraft mostly)
connects with V2 and V3, justifying the importance of texture information to classify the aircrafts.

V1

V2

Bird Texture Aircraft Fungi

V0 V3

Bird Blur Texture Blur Aircraft Blur Fungi Blur V7

V4

V6

V5

Bird Pencil Texture Pencil Aircraft Pencil Fungi Pencil


Figure 3: Interpretation of meta-knowledge graph on Art-Multi dataset. For each subdataset, we
randomly select one task from them. In the left, we show the similarity heatmap between prototypes
(P0-P5) and meta-knowledge vertices (V0-V7). In the right part, we show the meta-knowledge graph.

6 CONCLUSION

In this paper, to improve the effectiveness of meta-learning for handling heterogeneous task, we
propose a new framework called ARML, which automatically extract relation across tasks and
construct a meta-knowledge graph. When a new task comes in, it can quickly find the most relevant
relations through the meta-knowledge graph and use this knowledge to facilitate its training process.
The experiments demonstrate the effectiveness of our proposed algorithm.


-----

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A ALGORITHM IN META-TESTING PROCESS

**Algorithm 2 Meta-Testing Process of ARML**

**Require: Training data** _t_ [of a new task][ T][t]
_D[tr]_

1: Construct the prototype-based relational graph Rt by computing prototype in equation 2 and
weight in equation 4

2: Compute the similarity between each prototype and meta-knowledge vertice in equation 6 and
construct the super-graph St

3: Apply GNN on super-graph St and get the updated prototype representation **C[ˆ]** _Rt_

4: Aggregate CRt in equation 8, **C[ˆ]** _Rt in equation 9 and get the representations qt, tt_

5: Compute the task-specific initialization θ0t in equation 10
6: Update parameters θt = θ0t − _α∇θL(fθ, Dt[tr][)]_


B HYPERPARAMETERS SETTINGS

B.1 2D REGRESSION

In 2D regression problem, we set the inner-loop stepsize (i.e., α) and outer-loop stepsize (i.e., β) as
0.001 and 0.001, respectively. The embedding function E is set as one layer with 40 neurons. The
autoencoder aggregator is constructed by the gated recurrent structures. We set the meta-batch size as
25 and the inner loop gradient steps as 5.

B.2 FEW-SHOT IMAGE CLASSIFICATION

In few-shot image classification, for both Plain-Multi and Art-Multi datasets, we set the corresponding
inner stepsize (i.e., α) as 0.001 and the outer stepsize (i.e., β) as 0.01. For the embedding function E,
we employ two convolutional layers with 3 × 3 filters. The channel size of these two convolutional
layers are 32. After convolutional layers, we use two fully connected layers with 384 and 64 neurons
for each layer. Similar as the hyperparameter settings in 2D regression, the autoencoder aggregator
is constructed by the gated recurrent structures, i.e., AG[t], AG[t]dec [AG][q][,][ AG]dec[q] [are all GRUs. The]
meta-batch size is set as 4. For the inner loop, we use 5 gradient steps.

B.3 DETAILED BASELINE SETTINGS

For the gradient-based baselines (i.e., MAML, MetaSGD, MT-Net, BMAML. MUMOMAML,
HSML), we use the same inner loop stepsize and outer loop stepsize rate as our ARML. As for
non-parametric based meta-learning algorithms, both TADAM and Prototypical network, we use the
same meta-training and meta-testing process as gradient-based models. Additionally, TADAM uses
the same embedding function E as ARML for fair comparison (i.e., similar expressive ability).

C ADDITIONAL DISCUSSION OF DATASETS

In this dataset, we use pencil and blur filers to change the task distribution. To investigate the effect
of pencil and blur filters, we provide one example in Figure 4. We can observe that different filters
result in different data distributions. All used filter are provided by OpenCV[1].

D RESULTS ON MINIIMAGENET

For miniimagenet, since it do not have the characteristic of task heterogeneity, we show the results in
Table 5. In this table, we compare the MiniImagenet dataset with other gradient-based meta-learning
models (the first four baselines are globally shared models and the next four are task-specific models).
Similar as (Finn et al., 2018), we also apply the standard 4-block convolutional layers for each

1https://opencv.org/


-----

(a) : Plain Image (b) : with blur filter (c) : with pencil filter

Figure 4: Effect of different filters.

baseline. For MT-Net, we use the reported results in (Yao et al., 2019), which control the model with
the same expressive power. The results indicate that our proposed ARML can outperform the original
MAML and achieves comparable performance with task-specific models (e.g., MT-Net, PLATIPUS,
HSML). Most task-specific models achieve the similar performance on the standard benchmark due
to the homogeneity between tasks.

Table 5: Performance comparison on the 5-way, 1-shot MiniImagenet dataset.

|Algorithms|5-way 1-shot Accuracy|
|---|---|

|MAML (Finn et al., 2017) LLAMA (Finn & Levine, 2018) Reptile (Nichol & Schulman, 2018) MetaSGD (Li et al., 2017)|48.70 1.84% ± 49.40 1.83% ± 49.97 0.32% ± 50.47 1.87% ±|
|---|---|

|MT-Net (Lee & Choi, 2018) MUMOMAML (Vuorio et al., 2019) HSML (Yao et al., 2019) PLATIPUS (Finn et al., 2018)|49.75 1.83% ± 49.86 1.85% ± 50.38 1.85% ± 50.13 1.86% ±|
|---|---|

|ARML|50.42 1.73% ±|
|---|---|


E ADDITIONAL RESULTS OF FEW-SHOT IMAGE CLASSIFICATION

E.1 FULL OVERALL RESULTS TABLE OF ART-MULTI DATASET

We provide the full results table of Art-Multi Dataset in Table 9. In this table, we can see our proposed
ARML outperforms almost all baselines in every sub-datasets.

F FURTHER INVESTIGATION OF ABLATION STUDY

In this section, we first show the full evaluation results of model ablation study on Art-Multi dataset
in 6. Note that, for the tanh activation (ablation model V), the performance is similar as applying
the sigmoid activation. On some subdatasets, the results are even better. We choose the sigmoid
activation for ARML because it achieves overall better performance than the tanh activation on more
subdatasets. Then, for Plain-Multi dataset, we show the results in 7. The conclusion of ablation study
in Plain-Multi dataset is similar as the conclusion drawn from the results on Art-Multi dataset. The
improvement on these two datasets verifies the necessity of the joint framework in ARML.

G ADDITIONAL ANALYSIS OF META-KNOWLEDGE GRAPH

In this section, we add more interpretation analysis of meta-knowledge graph. First, we show the full
evaluation results of sensitivity analysis on Art-Multi dataset in Table 8.


-----

Table 6: Full evaluation results of model ablation study on Art-Multi dataset. B, T, A, F represent
bird, texture, aircraft, fungi, respectively. Plain means original image.

|Model|B Plain B Blur B Pencil T Plain T Blur T Pencil|
|---|---|


|I. no prototype-based graph II. no prototype|72.08% 71.06% 66.83% 45.23% 39.97% 41.67% 72.99% 70.92% 67.19% 45.17% 40.05% 41.04%|
|---|---|


|III. no meta-knowledge graph IV. no reconstruction loss|70.79% 69.53% 64.87% 43.37% 39.86% 41.23% 70.82% 69.87% 65.32% 44.02% 40.18% 40.52%|
|---|---|


|V. tanh VI. film|72.70% 69.53% 66.85% 45.81% 40.79% 38.64% 71.52% 68.70% 64.23% 43.83% 40.52% 39.49%|
|---|---|


|Model|A Plain A Blur A Pencil F Plain F Blur F Pencil|
|---|---|


|I. no prototype-based graph II. no prototype|70.06% 68.02% 60.66% 55.81% 54.39% 50.01% 71.10% 67.59% 61.07% 56.11% 54.82% 49.95%|
|---|---|


|III. no meta-knowledge graph IV. no reconstruction loss|69.97% 68.03% 59.72% 55.84% 53.72% 48.91% 66.83% 65.73% 55.98% 54.62% 53.02% 48.01%|
|---|---|


|V. tanh VI. film|73.96% 69.70% 60.75% 56.87% 54.30% 49.82% 69.13% 66.93% 55.59% 55.77% 53.72% 48.92%|
|---|---|


|ARML|71.89% 68.59% 61.41% 56.83% 54.87% 50.53%|
|---|---|


ARML **73.05%** **71.31%** **67.14%** 45.32% 40.15% **41.98%**


Table 7: Results of Model Ablation (5-way, 5-shot results) on Plain-Multi dataset.

|Ablation Models|Bird|Texture|Aircraft|Fungi|
|---|---|---|---|---|

|I. no sample-level graph II. no prototype|71.96 ± 0.72% 72.86 ± 0.74%|48.79 ± 0.67% 49.03 ± 0.69%|74.02 ± 0.65% 74.36 ± 0.65%|56.83 ± 0.80% 57.02 ± 0.81%|
|---|---|---|---|---|

|III. no meta-knowledge graph IV. no reconstruction loss|71.23 ± 0.75% 70.99 ± 0.74%|47.96 ± 0.68% 48.03 ± 0.69%|73.71 ± 0.69% 69.86 ± 0.66%|55.97 ± 0.82% 55.78 ± 0.83%|
|---|---|---|---|---|

|V. tanh VI. film|73.45 ± 0.71% 72.95 ± 0.73%|49.23 ± 0.66% 49.18 ± 0.69%|74.39 ± 0.65% 73.82 ± 0.68%|57.38 ± 0.80% 56.89 ± 0.80%|
|---|---|---|---|---|

|ARML|73.34 ± 0.70%|49.67 ± 0.67%|74.88 ± 0.64%|57.55 ± 0.82%|
|---|---|---|---|---|


Then, we analyze the meta-knowledge graph on Plain-Multi dataset by visualizing the learned metaknowledge graph on Plain-Multi dataset (as shown in Figure 5). In this figure, we can see that
different subdatasets activate different vertices. Specifically, V2, which is mainly activated by texture,
plays a significantly important role in aircraft and fungi. Thus, V2 connects with V3 and V1 in the
meta-knowledge graph, which are mainly activated by fungi and aircraft, respectively. In addition,
V0 is also activated by aircraft because of the similar contour between aircraft and bird. Furthermore,
in meta-knowledge graph, V0 connects with V3, which shows the similarity of environment between
bird images and fungi images.


-----

Bird


Texture


V1

V2

V0

V3


Aircraft Fungi

Figure 5: Interpretation of meta-knowledge graph on Plain-Multi dataset. For each subdataset, one
task is randomly selected from them. In the left figure, we show the similarity heatmap between
prototypes (P1-P5) and meta-knowledge vertices (denoted as E1-E4), where deeper color means
higher similarity. In the right part, we show the meta-knowledge graph, where a threshold is also set
to filter low similarity links.

Table 8: Full evaluation results of performance v.s. # vertices of meta-knowledge graph on Art-Multi.
B, T, A, F represent bird, texture, aircraft, fungi, respectively. Plain means original image.

|# of Vertices|B Plain B Blur B Pencil T Plain T Blur T Pencil|
|---|---|

|# of Vertices|A Plain A Blur A Pencil F Plain F Blur F Pencil|
|---|---|

|4 8 12 16 20|70.98% 67.36% 60.46% 56.07% 53.77% 50.08% 71.89% 68.59% 61.41% 56.83% 54.87% 50.53% 71.78% 67.26% 60.97% 56.87% 55.14% 50.86% 71.96% 68.55% 61.14% 56.76% 54.54% 49.41% 72.02% 68.29% 60.59% 55.95% 54.53% 50.13%|
|---|---|


4 72.29% 70.36% 67.88% 45.37% 41.05% 41.43%
8 73.05% 71.31% 67.14% 45.32% 40.15% 41.98%
12 73.45% 70.64% 67.41% 44.53% 41.41% 41.05%
16 72.68% 70.18% 68.34% 45.63% 41.43% 42.18%
20 73.41% 71.07% 68.64% 46.26% 41.80% 41.61%


-----

|55.27% 52.62% 48.58% 30.57% 28.65% 28.39% 45.59% 42.24% 34.52% 39.37% 38.58% 35.38% 55.23% 53.08% 48.18% 29.28% 28.70% 28.38% 51.24% 47.29% 35.98% 41.08% 40.38% 36.30% 56.99% 54.21% 50.25% 32.13% 29.63% 29.23% 43.64% 40.08% 33.73% 43.02% 42.64% 37.96% 57.73% 53.18% 50.96% 31.88% 29.72% 29.90% 49.95% 43.36% 39.61% 42.97% 40.08% 36.52% 58.15% 53.20% 51.09% 32.01% 30.21% 30.17% 49.98% 45.79% 40.87% 42.58% 41.29% 37.01%|53.67% 50.98% 46.66% 31.37% 29.08% 28.48% 45.54% 43.94% 35.49% 37.71% 38.00% 34.36% 54.76% 52.18% 48.85% 32.03% 29.90% 30.82% 50.42% 47.59% 40.17% 41.73% 40.09% 36.27%|59.67% 54.89% 52.97% 32.31% 30.77% 31.51% 51.99% 47.92% 41.93% 44.69% 42.13% 38.36%|
|---|---|---|
|MAML MetaSGD MT-Net MUMOMAML HSML|ProtoNet TADAM|ARML|

|71.51% 68.65% 63.93% 42.96% 39.59% 38.87% 64.68% 62.54% 49.20% 54.08% 52.02% 46.39% 71.31% 68.73% 64.33% 41.89% 37.79% 37.91% 64.88% 63.36% 52.31% 53.18% 52.26% 46.43% 71.18% 69.29% 68.28% 43.23% 39.42% 39.20% 63.39% 58.29% 46.12% 54.01% 51.70% 47.02% 71.57% 70.50% 64.57% 44.57% 40.31% 40.07% 63.36% 61.55% 52.17% 54.89% 52.82% 47.79% 71.75% 69.31% 65.62% 44.68% 40.13% 41.33% 70.12% 67.63% 59.40% 55.97% 54.60% 49.40%|70.42% 67.90% 61.82% 44.78% 38.43% 38.40% 65.84% 63.41% 54.08% 51.45% 50.56% 46.33% 70.08% 69.05% 65.45% 44.93% 41.80% 40.18% 70.35% 68.56% 59.09% 56.04% 54.04% 47.85%|73.05% 71.31% 67.14% 45.32% 40.15% 41.98% 71.89% 68.59% 61.41% 56.83% 54.87% 50.53%|
|---|---|---|
|MAML MetaSGD MT-Net MUMOMAML HSML|ProtoNet TADAM|ARML|


F Pencil

F Blur

F Plain
A Pencil

A Blur

A Plain
T Pencil

T Blur

T Plain
B Pencil

B Blur

B Plain

Algorithms
Settings


%
**36.38**

%
**13.42**

%
**69.44**

%
**93.41**

%
**92.47**

%
**99.51**

%
**51.31**

%
**77.30**

%
**31.32**

%
**97.52**

%
**89.54**

%
**67.59**

ARML


%
**53.50**

%
**87.54**

%
**83.56**

%
**41.61**

%
**59.68**

%
**89.71**

%
**98.41**
15%.40

%
**32.45**

%
**14.67**

%
**31.71**

%
**05.73**

ARML


-----