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深度神经网络在拥有大量数据集和足够的计算资源的情况下能够取得巨大的成功。然而，他们快速学习新概念的能力相当有限。元学习是解决这一问题的一种方法，通过使网络学会如何学习。令人兴奋的深度元学习领域正在高速发展，但缺乏对当前技术的统一、深刻的概述。这项工作就是这样。《深度元学习》综述论文（2020年）探讨了深度神经网络在学习新概念方面的局限性以及元学习作为解决策略的重要性。元学习，或称为“学习如何学习”，旨在使网络具备快速适应和学习新任务的能力，类似于人类智能的关键特征。这篇论文提供了深度元学习领域的统一和深入概述，涵盖了其主要方法和技术，并指出了未来的研究挑战。论文为读者构建了理论基础，介绍了元学习的基本原理。元学习方法通常分为三类：基于度量的、基于模型的和基于优化的技术。基于度量的方法关注于学习相似性度量，以便在少数样例上进行有效分类；基于模型的方法则涉及学习一个可以泛化到新任务的模型；而基于优化的方法则侧重于改进学习算法本身，使其能快速适应新环境。接着，论文深入研究并总结了这三大类方法中的关键技术和进展。例如，在基于度量的元学习中，研究可能包括嵌入空间的学习和距离函数的优化，以提高对新任务的泛化能力。基于模型的元学习可能涉及到动态网络结构的调整或生成式模型，以适应不同任务的需求。基于优化的元学习则可能探讨如何有效地初始化参数，或者设计更高效的训练策略，如学习率调度。尽管深度元学习已取得显著进步，但仍存在一些待解决的问题和挑战。论文指出，一个关键挑战是评估方法的异质性，即在不同类型的基准测试中验证元学习的性能。此外，目前的元学习方法往往计算成本高昂，需要大量的计算资源，这限制了它们在实际应用中的广泛采用。因此，降低计算成本是另一个重要的研究方向。此外，论文还讨论了元学习与迁移学习、 Few-shot 学习之间的关系。迁移学习利用已有的知识来加速新任务的学习，而Few-shot学习则是在极少量样本情况下学习新任务的能力，这两者都是元学习的子领域，并且相互补充。《深度元学习》综述论文提供了一个全面的视角，分析了深度神经网络通过元学习提升学习效率的可能性和当前的研究进展。随着元学习技术的不断发展，我们可以期待它将在减少数据需求、提高学习速度和解决复杂问题方面发挥更大的作用，进一步推动人工智能向着更接近人类智能的方向发展。

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A Survey of Deep Meta-Learning

Mike Huisman [email protected]

Jan N. van Rijn j.n.van.rijn@liacs.leidenuniv.nl

Aske Plaat a.plaat@liacs.leidenuniv.nl

Leiden Institute of Advanced Computer Science

Leiden University

Niels Bohrweg 1, 2333CA Leiden, The Netherlands

Abstract

Deep neural networks can achieve great successes when presented with large data sets and

suﬃcient computational resources. However, their ability to learn new concepts quickly is

quite limited. Meta-learning is one approach to address this issue, by enabling the network

to learn how to learn. The exciting ﬁeld of Deep Meta-Learning advances at great speed,

but lacks a uniﬁed, insightful overview of current techniques. This work presents just that.

After providing the reader with a theoretical foundation, we investigate and summarize key

methods, which are categorized into i) metric-, ii) model-, and iii) optimization-based tech-

niques. In addition, we identify the main open challenges, such as performance evaluations

on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.

Keywords: Meta-learning, Learning to learn, Few-shot learning, Transfer learning, Deep

learning

1. Introduction

In recent years, deep learning techniques have achieved remarkable successes on various

tasks, including game-playing (Mnih et al., 2013; Silver et al., 2016), image recognition

(Krizhevsky et al., 2012; He et al., 2015), and machine translation (Wu et al., 2016). Despite

these advances, ample challenges remain to be solved, such as the large amounts of data and

training that are needed to achieve good performance. These requirements severely constrain

the ability of deep neural networks to learn new concepts quickly, one of the deﬁning aspects

of human intelligence (Jankowski et al., 2011; Lake et al., 2017).

Meta-learning has been suggested as one strategy to overcome this challenge (Naik and

Mammone, 1992; Schmidhuber, 1987; Thrun, 1998). The key idea is that meta-learning

agents improve their own learning ability over time, or equivalently, learn to learn. The

learning process is primarily concerned with tasks (set of observations) and takes place at

two diﬀerent levels: an inner- and an outer-level. At the inner-level, a new task is presented,

and the agent tries to quickly learn the associated concepts from the training observations.

This quick adaptation is facilitated by knowledge that it has accumulated across earlier

tasks at the outer-level. Thus, whereas the inner-level concerns a single task, the outer-level

concerns a multitude of tasks.

Historically, the term meta-learning has been used with various scopes. In its broadest

sense, it encapsulates all systems that leverage prior learning experience in order to learn new

arXiv:2010.03522v1 [cs.LG] 7 Oct 2020

Huisman, van Rijn, and Plaat

tasks more quickly (Vanschoren, 2018). This broad notion includes more traditional algo-

rithm selection and hyperparameter optimization techniques for Machine Learning (Brazdil

et al., 2008). In this work, however, we focus on a subset of the meta-learning ﬁeld which de-

velops meta-learning procedures to learn a good inductive bias for (deep) neural networks.

Henceforth, we use the term Deep Meta-Learning to refer to this subﬁeld of meta-learning.

The ﬁeld of Deep Meta-Learning is advancing at a quick pace, while it lacks a coherent,

unifying overview, providing detailed insights into the key techniques. Vanschoren (2018)

has surveyed meta-learning techniques, where meta-learning was used in the broad sense,

limiting its account of Deep Meta-Learning techniques. Also, many exciting developments

in deep meta-learning have happened after the survey was published. A more recent survey

by Hospedales et al. (2020) adopts the same notion of deep meta-learning as we do, but

aims for a broad overview, omitting technical details of the various techniques.

We attempt to ﬁll this gap by providing detailed explications of contemporary Deep

Meta-Learning techniques, using a uniﬁed notation. In addition, we identify current chal-

lenges and directions for future work. More speciﬁcally, we cover modern techniques in the

ﬁeld for supervised and reinforcement learning, that have achieved state-of-the-art perfor-

mance, obtained popularity in the ﬁeld, and presented novel ideas. Extra attention is paid

to MAML (Finn et al., 2017), and related techniques, because of their impact on the ﬁeld.

This work can serve as educational introduction to the ﬁeld of Deep Meta-Learning, and as

reference material for experienced researchers in the ﬁeld. Throughout, we will adopt the

taxonomy used by Vinyals (2017), which identiﬁes three categories of Deep Meta-Learning

approaches: i) metric-, ii) model-, and iii) optimization-based meta-learning techniques.

The remainder of this work is structured as follows. Section 2 builds a common founda-

tion on which we will base our overview of Deep Meta-Learning techniques. Section 3, 4, and

5 cover the main metric-, model-, and optimization-based meta-learning techniques, respec-

tively. Section 6 provides a helicopter view of the ﬁeld, and summarizes the key challenges

and open questions. Table 1 gives an overview of notation that we will use throughout this

paper.

2. Foundation

In this section, we build the necessary foundation for investigating Deep Meta-Learning

techniques in a consistent manner. To begin with, we contrast regular learning and meta-

learning. Afterwards, we brieﬂy discuss how Deep Meta-Learning relates to diﬀerent ﬁelds,

what the usual training and evaluation procedure looks like, and which benchmarks are often

used for this purpose. We ﬁnish this section by describing some applications and context of

the meta-learning ﬁeld.

2.1 The Meta Abstraction

In this subsection, we contrast base-level (regular) learning and meta-learning for two dif-

ferent paradigms, i.e., supervised and reinforcement learning.

1. Here, inductive bias refers to the assumptions of a model which guide predictions on unseen data

(Mitchell, 1980).

A Survey of Deep Meta-Learning

Expression Meaning

Meta-learning Learning meta-knowledge that can be used to learn new tasks more quckly

= (D

, D

test

) A task consisting of a labeled train and test set

Support set The train set D

associated with a task T

Query set The test set D

test

associated with a task T

Example input vector i in the support set

(One-hot encoded) label of example input x

from the support set

x Input in the query set

y A (one-hot encoded) label for input x

(f/g/h)

◦

Neural network function with parameters ◦

Inner-level At the level of a single task

Outer-level At meta-level: across tasks

Fast weights Parameters that were generated for a speciﬁc task/example

Base-learner Learner that works at the inner-level

Meta-learner Learner that operates at the outer-level

Input embedding Activation pattern in the ﬁnal layer of a neural network caused by the input

Task embedding An internal representation of a task in a network/system

SL Supervised Learning

RL Reinforcement Learning

Table 1: Some notation and meaning, which we use throughout this paper.

2.1.1 Regular Supervised Learning

In supervised learning, we wish to learn a function f

: X → Y that learns to map inputs

∈ X to their corresponding outputs y

∈ Y . Here, θ are model parameters (e.g. weights

in a neural network) that determine the function’s behavior. To learn these parameters, we

are given a data set of m observations: D = {(x

, y

)}

i=1

. Thus, given a data set D, learning

boils down to ﬁnding the correct setting for θ that minimizes an empirical loss function L

which must capture how the model is performing, such that appropriate adjustments to its

parameters can be made. In short, we wish to ﬁnd

:= arg min

(θ), (1)

where SL stands for “supervised learning". Note that this objective is speciﬁc to data set

D, meaning that our model f

may not generalize to examples outside of D. To measure

generalization, one could evaluate the performance on a separate test data set, which contains

unseen examples. A popular way to do this is through cross-validation, where one repeatedly

creates train and test splits D

, D

test

⊂ D and uses these to train and evaluate a model

respectively (Hastie et al., 2009).

Finding globally optimal parameters θ

is often computationally infeasible. We can,

however, approximate them, guided by pre-deﬁned meta-knowledge ω (Hospedales et al.,

2020), which includes, e.g., the initial model parameters θ, choice of optimizer, and learning

Huisman, van Rijn, and Plaat

rate schedule. As such, we approximate

≈ g

(D, L

), (2)

where g

is an optimization procedure that uses pre-deﬁned meta-knowledge ω, data set D,

and loss function L

, to produce updated weights g

(D, L

) that (presumably) perform

well on D.

2.1.2 Supervised Meta-Learning

In contrast, supervised meta-learning does not assume that any meta-knowledge ω is given,

or pre-deﬁned. Instead, the goal of meta-learning is to ﬁnd the best ω, such that our

(regular) base-learner can learn new tasks (data sets) as quickly as possible. Thus, whereas

supervised regular learning involves one data set, supervised meta-learning involves a group

of data sets. The goal is to learn meta-knowledge ω such that our model can learn many

diﬀerent tasks well. Thus, our model is learning to learn.

More formally, we have a probability distribution of tasks p(T ), and wish to ﬁnd optimal

meta-knowledge

∗

:= arg min

vp(T )

| {z }

Outer-level

, L

))

| {z }

Inner-level

]. (3)

Here, the inner-level concerns task-speciﬁc learning, while the outer-level concerns multiple

tasks. One can now easily see why this is meta-learning: we learn ω, which allows for quick

learning of tasks T

at the inner-level. Hence, we are learning to learn.

2.1.3 Regular Reinforcement Learning

In reinforcement learning, we have an agent that learns from experience. That is, it interacts

with an environment, modeled by a Markov Decision Process (MDP) M = (S, A, P, r, p

, γ, T ).

Here, S is the set of states, A the set of actions, P the transition probability distribution

deﬁning P (s

t+1

, a

), r : S × A → R the reward function, p

the probability distribution

over initial states, γ ∈ [0, 1] the discount factor, and T the time horizon (maximum number

of time steps) (Sutton and Barto, 2018; Duan et al., 2016).

At every time step t, the agent ﬁnds itself in state s

, in which the agent performs an

action a

, computed by a policy function π

(i.e., a

= π

)), which is parameterized by

weights θ. In turn, it receives a reward r

= r(s

, π

)) ∈ R and a new state s

t+1

. This

process of interactions continues until a termination criterion is met (e.g. ﬁxed time horizon

T reached). The goal of the agent is to learn how to act in order to maximize its expected

reward. The reinforcement learning (RL) goal is to ﬁnd

:= arg min

traj

t=0

r(s

, π

)), (4)

where we take the expectation over the possible trajectories traj = (s

, π

), ...s

, π

))

due to the random nature of MDPs (Duan et al., 2016). Note that γ is a hyperparameter

that can prioritize short- or long-term rewards by decreasing or increasing it, respectively.

A Survey of Deep Meta-Learning

Also in case of reinforcement learning it is often infeasible to ﬁnd the global optimum θ

and thus we settle for approximations. In short, given a learning method ω, we approximate

≈ g

, L

), (5)

where again T

is the given MDP, and g

is the optimization algorithm, guided by pre-deﬁned

meta-knowledge ω.

Note that in a Markov Decision Process (MDP), the agent knows the state at any given

time step t. When this is not the case, it becomes a Partially Observable Markov Decision

Process (POMDP), where the agent receives only observations O, and uses these to update

its belief with regard to the state it is in (Sutton and Barto, 2018).

2.1.4 Meta Reinforcement Learning

The meta abstraction has as its object a group of tasks, or Markov Decision Processes

(MDPs) in the case of reinforcement learning. Thus, instead of maximizing the expected

reward on a single MDP, the meta reinforcement learning objective is to maximize the

expected reward over various MDPs, by learning meta-knowledge ω. Here, the MDPs are

sampled from some distribution p(T ). So now, we wish to ﬁnd a set of parameters

∗

:= arg min

vp(T )

| {z }

Outer-level







traj

t=0

r(s

, π

)

))

| {z }

Inner-level







. (6)

2.1.5 Contrast with other Fields

Now that we have provided a formal basis for our discussion for both supervised and rein-

forcement meat-learning, it is time to contrast meta-learning brieﬂy with two related areas

of machine learning that also have the goal to improve the speed of learning. We will start

with transfer learning.

Transfer Learning In Transfer Learning, one tries to transfer knowledge of previous

tasks to new, unseen tasks (Pan and Yang, 2009; Taylor and Stone, 2009). As such, it

subsumes meta-learning, where we attempt to leverage meta-knowledge to learn new tasks

more quickly. A key property of meta-learning techniques is their meta-objective, which

explicitly aims to optimize performance across a distribution over tasks (as seen in previous

sections by taking the expected loss over a distribution of tasks). This objective need not

always be present in Transfer Learning techniques, e.g., when one pre-trains a model on a

large data set, and ﬁne-tunes the learned weights on a smaller data set.

Multi-task learning An other, closely related ﬁeld, is that of multi-task learning.

In multi-task learning a model is jointly trained to perform well on multiple ﬁxed tasks

(Hospedales et al., 2020). Meta-learning, in contrast, aims to ﬁnd a model that can learn

new (previously unseen) tasks quickly. This diﬀerence is illustrated in Figure 1.

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