---
title: "Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images"
authors:
  - "Wentao Chen"
  - "Chenyang Si"
  - "Wei Wang"
  - "Liang Wang"
  - "Zilei Wang"
  - "Tieniu Tan"
doi: "10.24963/ijcai.2021/313"
url: "https://ijcai.org/proceedings/2021/313"
pdf: "https://ijcai.org/proceedings/2021/0313.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "2271-2277"
track: "Main Track"
keywords:
  - "Machine Learning: Classification"
  - "Machine Learning: Deep Learning"
  - "Machine Learning: Unsupervised Learning"
---

# Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images

Wentao Chen, Chenyang Si, Wei Wang, Liang Wang, Zilei Wang, Tieniu Tan

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 2271-2277. DOI: [10.24963/ijcai.2021/313](https://doi.org/10.24963/ijcai.2021/313)

## Abstract

Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class. One way to alleviate this problem is to acquire a strong inductive bias via meta-learning on similar tasks. In this paper, we show that such inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes.   Specifically, we propose a novel part-based self-supervised representation learning scheme to learn transferable representations by maximizing the similarity of an image to its discriminative part. To mitigate the overfitting in few-shot classification caused by data scarcity, we further propose a part augmentation strategy by retrieving extra images from a base dataset. We conduct systematic studies on miniImageNet and tieredImageNet benchmarks. Remarkably, our method yields impressive results, outperforming the previous best unsupervised methods by 7.74% and 9.24% under 5-way 1-shot and 5-way 5-shot settings, which are comparable with state-of-the-art supervised methods.