👆 右上角点击 告诉我，你希望这个项目继续加速更新迭代 ❤️ & ☕️

Meta-Learning-Papers-with-Code

This repository contains a reading list of papers with code on Meta-Learning and Meta-Reinforcement-Learning, These papers are mainly categorized according to the type of model. In addition, I will separately list papers from important conferences starting from 2023, e.g., NIPS, ICML, ICLR, CVPR etc. This repository is still being continuously improved. If you have found any relevant papers that need to be included in this repository, please feel free to submit a pull request (PR) or open an issue.

Each paper may be applicable to one or more types of meta-learning frameworks, including optimization-based and metric-based, and may be applicable to multiple data sources, including image, text, audio, video, and multi-modality. These are marked in the type column. In addition, for different tasks and different problems, we have marked the SOTA algorithm separately. This is submitted with reference to the leadboard at the time of submission, and will be continuously modified. We provide a basic introduction to each paper to help you understand the work and core ideas of this article more quickly.

Label

🎭 Different Frameworks

• Meta-Learning.
• Meta-Reinforcement-Learning.

🎨 Different Types

• Optimization-based meta-learning approaches acquire a collection of optimal initial parameters, facilitating rapid convergence of a model when adapting to novel tasks.
• Metric-based meta-learning approaches acquire embedding functions that transform instances from various tasks, allowing them to be readily categorized using non-parametric methods.

✨ Different Data Sources

• Meta-Learning for CV (Images)
• Meta-Learning for CV (Videos)
• Meta-Learning for NLP
• Meta-Learning for Audio
• Meta-Learning for Multi-modal

It is worth noting that the experiments of some frameworks consist of multiple data sources. Our annotations are based on the paper description.

🎁 Notice

• The paper does not provide code, I will write it myself and supplement it later.
• The Oral paper.
• The Oral paper.

🚩 I have marked some recommended papers with 🌟/🎈 (SOTA methods/Just my personal preference 😉).

🚩 I will maintain three hours of paper reading, code repository maintenance and entry supplement every day 😉).

🚩 All papers are provided in the corresponding folders 😉.

Topics

• Survey
• Optimization
• Theory
• Domain generalization
• Lifelong learning
• Configuration transfer
• Model compression
• Summary of conference papers
  • CVPR23
  • ICML23
  • NIPS23
  • ICLR23
• Libraries
• Blogs
• [Lecture Videos](#Lecture Videos)
• Datasets
• Researchers

Survey.

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2018	RL L2L		arXiv 2018	A review of meta-reinforcement learning for deep neural networks architecture search	None
2019	Book of Meta-Learning		Book	Meta-Learning (Automated Machine Learning)	None
2019	Learn dynamics		arXiv 2019	Meta-learners' learning dynamics are unlike learners'	None
2020	NLP🌟		arXiv 2020	Meta-learning for few-shot natural language processing: A survey	None
2020	CV-classifier		IEEE Access	A literature survey and empirical study of meta-learning for classifier selection	None
2020	RL DL L2L		arXiv 2020	A comprehensive overview and survey of recent advances in meta-learning	None
2021	Learn 2 Learn		arXiv 2021	Meta-Learning: A Survey	None
2021	Learn 2 Learn 🎈		TPAMI	Meta-Learning in Neural Networks: A Survey	None
2021	Learn 2 Learn		Artif Intell Rev	A survey of deep meta-learning	None
2021	Learn 2 Learn		Current Opinion in Behavioral Sciences	Meta-learning in natural and artificial intelligence	None
2022	Multi-Modal🌟		KBS	Multimodality in meta-learning: A comprehensive survey	None
2022	Image Segmentation🌟		PR	Meta-seg: A survey of meta-learning for image segmentation	None
2022	Cyberspace Security		Digit. Commun. Netw.	Application of meta-learning in cyberspace security: A survey	None
2023	RL L2L🌟		arXiv 2023	A survey of meta-reinforcement learning	None

Optimization

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2016	Reversible		ICML 2016	Gradient-based Hyperparameter Optimization through Reversible Learning	CODE
2017	MRL-GPS		ICLR 2017	Learning to Optimize
2019	L2G		arXiv 2019	Learning to Generalize to Unseen Tasks with Bilevel Optimization
2019	LOIS		arXiv 2019	Learning to Optimize in Swarms	CODE
2019	iMAML🌟		NIPS 2019	Meta-Learning with Implicit Gradients	CODE
2019	Xfer🌟		ICLR 2019	Transferring Knowledge across Learning Processes	CODE
2019	MetaInit		ICLR 2019	MetaInit: Initializing learning by learning to initialize
2019	Runge-Kutta-MAML		arXiv 2019	MetaInit: Initializing learning by learning to initialize
2020	WarpGrad		ICLR 2020	Model-Agnostic Meta-Learning using Runge-Kutta Methods	CODE
2022	Sharp-MAML🎈		ICML 2022	Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning	CODE
2022	BMG🌟		ICLR 2022	Bootstrapped Meta-Learning

Theory

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2018	MLAP		ICML 2018	Meta-learning by adjusting priors based on extended PAC-Bayes theory	CODE
2018	learning algorithm approximation		ICLR 2018	Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm
2018	ConsiderMRL		ICLR 2018	Some Considerations on Learning to Explore via Meta-Reinforcement Learning	CODE
2022	UMAML		ICLR 2022	Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
2022	TRGB		ICLR 2022	Task Relatedness-Based Generalization Bounds for Meta Learning
2021	PAC-Bayes		NeurIPS 2021	How Tight Can PAC-Bayes be in the Small Data Regime?
2021	meta_tr_val_split		ICML 2021	A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning	CODE
2021	stocBiO		ICML 2021	Bilevel Optimization: Convergence Analysis and Enhanced Design	CODE
2022	First active ML		AISTATS 2022	Near-Optimal Task Selection with Mutual Information for Meta-Learning
2022	LTR		AISTATS 2022	Learning Tensor Representations for Meta-Learning
2022	BayesianMAML or MAML?		AISTATS 2022	Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?
2023	​​What learning algorithm is in-context learning?		ICLR 2023	​​What learning algorithm is in-context learning? Investigations with linear models	CODE

Domain generalization

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2018	L2G		AAAI 2018	Learning to Generalize: Meta-Learning for Domain Generalization	CODE
2019	MASF		NIPS 2019	Domain Generalization via Model-Agnostic Learning of Semantic Features	CODE
2020	MLCA		ICLR 2020	Meta-learning curiosity algorithms	CODE

Lifelong learning

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2018	IL2L🌟		arXiv 2018	Incremental Learning-to-Learn with Statistical Guarantees
2019	VividNet		arXiv 2019	A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-Learning	CODE
2019	HSML		ICML 2019	Hierarchically Structured Meta-learning	CODE
2019	Online-ML🌟		ICML 2019	Online Meta-Learning
2019	MRCL		NIPS 2019	Meta-Learning Representations for Continual Learning	CODE
2019	Bayes-MAML		NIPS 2019	Reconciling meta-learning and continual learning with online mixtures of tasks
2019	ONL-ONL		NIPS 2019	Online-Within-Online Meta-Learning	CODE
2021	LWTL🌟		NIPS 2021	Learning where to learn: Gradient sparsity in meta and continual learning	CODE
2021	MARK🌟		NIPS 2021	Optimizing Reusable Knowledge for Continual Learning via Metalearning	CODE

Model compression

Summary of conference papers

CVPR23

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2023	PPL🌟		CVPR 2023	A Meta-Learning Approach to Predicting Performance and Data Requirements
2023	Meta-Explore		CVPR 2023	Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding
2023	Meta-Tuning🌟		CVPR 2023	Meta-Tuning Loss Functions and Data Augmentation for Few-Shot Object Detection
2023	Meta-Causal-learning🌟		CVPR 2023	Meta-Tuning Loss Functions and Data Augmentation for Few-Shot Object Detection
2023	Model-Scale Agnostic🌟		CVPR 2023	Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning.	CODE

ICML23

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2023	MLPS🌟		ICML 2023	Meta-Learning Parameterized Skills	CODE
2023	Meta-Meta-Learning		ICML 2023	Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning	CODE
2023	BiDf-MKD🌟		ICML 2023	Learning to Learn from APIs: Black-Box Data-Free Meta-Learning	CODE
2023	Meta-SAGE		ICML 2023	Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization	CODE
2023	RepVerb		ICML 2023	Effective Structured Prompting by Meta-Learning and Representative Verbalizer
2023	Memory-Based Meta-Learning		ICML 2023	Memory-Based Meta-Learning on Non-Stationary Distributions	CODE

NIPS23

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2023	Conformal-Meta🌟		NIPS 2023	Conformal Meta-learners for Predictive Inference of Individual Treatment Effects	CODE
2023	MGDD		NIPS 2023	Online Constrained Meta-Learning: Provable Guarantees for Generalization
2023	PINNs		NIPS 2023	MGDD: A Meta Generator for Fast Dataset Distillation
2023	OCML		NIPS 2023	Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
2023	Online Control		NIPS 2023	Online Control for Meta-optimization
2023	SCARF		NIPS 2023	Prefix-Tree Decoding for Predicting Mass Spectra from Molecules	CODE
2023	HNPs		NIPS 2023	Learning from Active Human Involvement through Proxy Value Propagation
2023	Zero-shot causal learning		NIPS 2023	Episodic Multi-Task Learning with Heterogeneous Neural Processes	CODE
2023	Zero-shot causal learning		NIPS 2023	Zero-shot causal learning
2023	Structure-free Graph Condensation		NIPS 2023	Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
2023	Pick-up-to-Learn		NIPS 2023	The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
2023	SimFBO		NIPS 2023	SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
2023	EmbodiedGPT		NIPS 2023	EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought	CODE

ICLR23

Date	Method	Type	Conference	Paper Title and Paper Interpretation	Code
2023	Transfer NAS		ICLR 2023	Transfer NAS with Meta-learned Bayesian Surrogates
2023	Betty🌟		ICLR 2023	Betty: An Automatic Differentiation Library for Multilevel Optimization	CODE
2023	​​What learning algorithm is in-context learning?		ICLR 2023	​​What learning algorithm is in-context learning? Investigations with linear models	CODE
2023	Learnable Behavior Control🌟		ICLR 2023	Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection!
2023	Metadata Archaeology		ICLR 2023	Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
2023	CMDP-within-online		ICLR 2023	A CMDP-within-online framework for Meta-Safe Reinforcement Learning
2023	MARS		ICLR 2023	MARS: Meta-learning as Score Matching in the Function Space

Libraries

Link
Awesome-META+
Higher by Facebook research
TorchMeta
Learn2learn

Blogs

Link
Multiple Meta-learning Papers
Berkeley Artificial Intelligence Research blog
Meta-Learning: Learning to Learn Fast
Meta-Reinforcement Learning
How to train your MAML: A step by step approach
An Introduction to Meta-Learning
From zero to research — An introduction to Meta-learning
What’s New in Deep Learning Research: Understanding Meta-Learning
Meta Reinforcement Learning Blog by Lilian Weng

Lecture Videos

Link
Stanford CS330: Multi-Task and Meta-Learning, 2019 by Chelsea Finn
Meta Learning lecture by Soheil Feizi
Chelsea Finn: Building Unsupervised Versatile Agents with Meta-Learning
Sam Ritter: Meta-Learning to Make Smart Inferences from Small Data
Model Agnostic Meta Learning by Siavash Khodadadeh
Meta Learning by Siraj Raval
Meta Learning by Hugo Larochelle
Meta Learning and One-Shot Learning

Datasets

Link
Omniglot
mini-ImageNet
ILSVRC
FGVC aircraft
Caltech-UCSD Birds-200-2011
Check several other datasets by Google here.

Workshops

Link
MetaLearn 2017
MetaLearn 2018
MetaLearn 2019
MetaLearn 2020

Researchers

Link
Chelsea Finn, UC Berkeley
Pieter Abbeel, UC Berkeley
Erin Grant, UC Berkeley
Raia Hadsell, DeepMind
Misha Denil, DeepMind
Adam Santoro, DeepMind
Sachin Ravi, Princeton University
David Abel, Brown University
Brenden Lake, Facebook AI Research