# Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

arXiv: https://arxiv.org/abs/2004.05805 · HTML: https://mlsanity.com/paper/2004.05805/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning](https://mlsanity.com/paper/2102.03837/) (2021)
- [Self-Supervision Can Be a Good Few-Shot Learner](https://mlsanity.com/paper/2207.09176/) (2022)
- [Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning](https://mlsanity.com/paper/2101.09499/) (2021)
- [GeNIe: Generative Hard Negative Images Through Diffusion](https://mlsanity.com/paper/2312.02548/) (2023)
- [Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning](https://mlsanity.com/paper/2209.13635/) (2022)
- [Trainable Class Prototypes for Few-Shot Learning](https://mlsanity.com/paper/2106.10846/) (2021)
- [Few-Shot Electronic Health Record Coding through Graph Contrastive Learning](https://mlsanity.com/paper/2106.15467/) (2021)

## Abstract (arXiv)

Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current works heavily rely on a large-scale labeled auxiliary set to train their models in an episodic-training paradigm. Such a kind of supervised setting basically limits the widespread use of few-shot learning algorithms. Instead, in this paper, we develop a novel framework called Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation (ULDA), which pays attention to the distribution diversity inside each constructed pretext few-shot task when using data augmentation. Importantly, we highlight the value and importance of the distribution diversity in the augmentation-based pretext few-shot tasks, which can effectively alleviate the overfitting problem and make the few-shot model learn more robust feature representations. In ULDA, we systemically investigate the effects of different augmentation techniques and propose to strengthen the distribution diversity (or difference) between the query set and support set in each few-shot task, by augmenting these two sets diversely (i.e., distribution shifting). In this way, even incorporated with simple augmentation techniques (e.g., random crop, color jittering, or rotation), our ULDA can produce a significant improvement. In the experiments, few-shot models learned by ULDA can achieve superior generalization performance and obtain state-of-the-art results in a variety of established few-shot learning tasks on Omniglot and miniImageNet. The source code is available in https://github.com/WonderSeven/ULDA.