# Few-Shot Learning with Class Imbalance

arXiv: https://arxiv.org/abs/2101.02523 · HTML: https://mlsanity.com/paper/2101.02523/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 89 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 30 of 46)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [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)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [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)
- [Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples](https://mlsanity.com/paper/1903.03096/) (2019)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning](https://mlsanity.com/paper/1805.10002/) (2018)
- [SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning](https://mlsanity.com/paper/1911.04623/) (2019)
- [Bayesian Model-Agnostic Meta-Learning](https://mlsanity.com/paper/1806.03836/) (2018)
- [Federated Meta-Learning with Fast Convergence and Efficient Communication](https://mlsanity.com/paper/1802.07876/) (2018)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Online Meta-Learning](https://mlsanity.com/paper/1902.08438/) (2019)
- [Towards a Neural Statistician](https://mlsanity.com/paper/1606.02185/) (2016)
- [Incremental Few-Shot Learning with Attention Attractor Networks](https://mlsanity.com/paper/1810.07218/) (2018)
- [Meta-Learning Requires Meta-Augmentation](https://mlsanity.com/paper/2007.05549/) (2020)
- [Few-Shot Learning Through an Information Retrieval Lens](https://mlsanity.com/paper/1707.02610/) (2017)
- [Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks](https://mlsanity.com/paper/1905.12917/) (2019)

## Built on by (most cited first, 18 of 18)

- [FLEX: Unifying Evaluation for Few-Shot NLP](https://mlsanity.com/paper/2107.07170/) (2021)
- [Realistic Evaluation of Transductive Few-Shot Learning](https://mlsanity.com/paper/2204.11181/) (2022)
- [MetaAudio: A Few-Shot Audio Classification Benchmark](https://mlsanity.com/paper/2204.02121/) (2022)
- [Prototypes-oriented Transductive Few-shot Learning with Conditional Transport](https://mlsanity.com/paper/2308.03047/) (2023)
- [Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2305.10648/) (2023)
- [Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation](https://mlsanity.com/paper/2509.06337/) (2025)
- [Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification](https://mlsanity.com/paper/2206.09843/) (2022)
- [Towards Few-Shot Learning in the Open World: A Review and Beyond](https://mlsanity.com/paper/2408.09722/) (2024)
- [Toward Unified Fine-Grained Vehicle Classification and Automatic License Plate Recognition](https://mlsanity.com/paper/2604.05271/) (2026)
- [DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade](https://mlsanity.com/paper/2305.14751/) (2023)
- [MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining](https://mlsanity.com/paper/2509.06806/) (2025)
- [MODIS: Multi-Omics Data Integration for Small and unpaired datasets](https://mlsanity.com/paper/2503.18856/) (2025)
- [Label Agnostic Pre-training for Zero-shot Text Classification](https://mlsanity.com/paper/2305.16521/) (2023)
- [Reading ability detection using eye-tracking data with LSTM-based few-shot learning](https://mlsanity.com/paper/2409.08798/) (2024)
- [Compressing Model with Few Class-Imbalance Samples: An Out-of-Distribution Expedition](https://mlsanity.com/paper/2502.05832/) (2025)
- [Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques](https://mlsanity.com/paper/2509.07605/) (2025)
- [DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification](https://mlsanity.com/paper/2603.12905/) (2026)
- [Retrieval-Augmented Detection of Potentially Abusive Clauses in Chilean Terms of Service](https://mlsanity.com/paper/2605.26019/) (2026)

## Abstract (arXiv)

Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the real-world dynamics where classes commonly occur at different frequencies. While it is generally understood that class imbalance harms the performance of supervised methods, limited research examines the impact of imbalance on the FSL evaluation task. Our analysis compares 10 state-of-the-art meta-learning and FSL methods on different imbalance distributions and rebalancing techniques. Our results reveal that 1) some FSL methods display a natural disposition against imbalance while most other approaches produce a performance drop by up to 17% compared to the balanced task without the appropriate mitigation; 2) contrary to popular belief, many meta-learning algorithms will not automatically learn to balance from exposure to imbalanced training tasks; 3) classical rebalancing strategies, such as random oversampling, can still be very effective, leading to state-of-the-art performances and should not be overlooked; 4) FSL methods are more robust against meta-dataset imbalance than imbalance at the task-level with a similar imbalance ratio (ρ<20), with the effect holding even in long-tail datasets under a larger imbalance (ρ=65).