# Meta-learning approaches for few-shot learning: A survey of recent advances

arXiv: https://arxiv.org/abs/2303.07502 · HTML: https://mlsanity.com/paper/2303.07502/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 134 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 30 of 71)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [Meta-Learning with Differentiable Convex Optimization](https://mlsanity.com/paper/1904.03758/) (2019)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [A simple neural network module for relational reasoning](https://mlsanity.com/paper/1706.01427/) (2017)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples](https://mlsanity.com/paper/1903.03096/) (2019)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [Probabilistic Model-Agnostic Meta-Learning](https://mlsanity.com/paper/1806.02817/) (2018)
- [Bayesian Model-Agnostic Meta-Learning](https://mlsanity.com/paper/1806.03836/) (2018)
- [Recasting Gradient-Based Meta-Learning as Hierarchical Bayes](https://mlsanity.com/paper/1801.08930/) (2018)
- [Task-Agnostic Meta-Learning for Few-shot Learning](https://mlsanity.com/paper/1805.07722/) (2018)
- [Transferrable Prototypical Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1904.11227/) (2019)
- [Towards a Neural Statistician](https://mlsanity.com/paper/1606.02185/) (2016)

## Built on by (most cited first, 25 of 25)

- [Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy](https://mlsanity.com/paper/2406.16087/) (2024)
- [A Comprehensive Review of Few-shot Action Recognition](https://mlsanity.com/paper/2407.14744/) (2024)
- [Deep Learning in Concealed Dense Prediction](https://mlsanity.com/paper/2504.10979/) (2025)
- [Generalization-Enhanced Few-Shot Object Detection in Remote Sensing](https://mlsanity.com/paper/2501.02474/) (2025)
- [Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data](https://mlsanity.com/paper/2505.14272/) (2025)
- [Fusing Cross-Domain Knowledge from Multimodal Data to Solve Problems in the Physical World](https://mlsanity.com/paper/2506.03155/) (2025)
- [STAND: Self-Aware Precondition Induction for Interactive Task Learning](https://mlsanity.com/paper/2409.07653/) (2024)
- [TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction](https://mlsanity.com/paper/2506.00453/) (2025)
- [IDRBench: Understanding the Capability of Large Language Models on Interdisciplinary Research](https://mlsanity.com/paper/2507.15736/) (2025)
- [CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2509.11219/) (2025)
- [Meta-Learning Linear Models for Molecular Property Prediction](https://mlsanity.com/paper/2509.13527/) (2025)
- [Riemannian Geometric-based Meta Learning](https://mlsanity.com/paper/2503.10993/) (2025)
- [FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition](https://mlsanity.com/paper/2503.18998/) (2025)
- [Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey](https://mlsanity.com/paper/2505.07882/) (2025)
- [MetaLab: Few-Shot Game Changer for Image Recognition](https://mlsanity.com/paper/2507.22057/) (2025)
- [Color as the Impetus: Transforming Few-Shot Learner](https://mlsanity.com/paper/2507.22136/) (2025)
- [Cross-Domain Few-Shot Learning via Multi-View Collaborative Optimization with Vision-Language Models](https://mlsanity.com/paper/2508.12861/) (2025)
- [A Domain-Adapted Lightweight Ensemble for Resource-Efficient Few-Shot Plant Disease Classification](https://mlsanity.com/paper/2512.13428/) (2025)
- [Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery](https://mlsanity.com/paper/2602.17605/) (2026)
- [Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent](https://mlsanity.com/paper/2602.19837/) (2026)
- [From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning](https://mlsanity.com/paper/2604.06262/) (2026)
- [Adaptive Learning Strategies for AoA-Based Outdoor Localization: A Comprehensive Framework](https://mlsanity.com/paper/2605.05055/) (2026)
- [Systematic Literature Review of Machine Learning Models and Applications for Text Recognition](https://mlsanity.com/paper/2608.26500/) (2026)
- [A Data-Driven Multimodal Method for Early Detection of Coordinated Abnormal Behaviors in Live-Streaming Platforms](https://mlsanity.com/paper/2609.01649/) (2026)
- [Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions](https://mlsanity.com/paper/2609.10851/) (2026)

## Abstract (arXiv)

Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods and recent advances in: (I) metric-based, (II) memory-based, (III), and learning-based methods. Finally, current challenges and insights for future researches are discussed.