Self-Supervised Learning for Few-Shot Image Classification

By Da Chen, YueFeng Chen, Yuhong Li, Feng Mao, Yuan He, Hui Xue. 2019

132 citations recorded by the source corpus.

Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the meta-learning process with episodic tasks and can fast adapt to class from training to testing. Due to the limited number of samples for each task, the initial embedding network for meta-learning becomes an essential component and can largely affect the performance in practice. To this end, most of the existing methods highly rely on the efficient embedding network. Due to the limited labelled data, the scale of embedding network is constrained under a supervised learning(SL) manner which becomes a bottleneck of the few-shot learning methods. In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself. We evaluate our work by extensive comparisons with previous baseline methods on two few-shot classification datasets (i.e., MiniImageNet and CUB) and achieve better performance over baselines. Tests on four datasets in cross-domain few-shot learning classification show that the proposed method achieves state-of-the-art results and further prove the robustness of the proposed model. Our code is available at https://github.com/phecy/SSL-FEW-SHOT.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Katja Hofmann
  2. Daniela Massiceti
  3. Edward Cutrell
  4. Simone Stumpf
  5. Luisa M. Zintgraf
  6. John F. Bronskill
  7. Chelsea Finn
  8. Sergey Levine

In Computer Vision and Pattern Recognition

  1. Cecily Morrison
  2. Katja Hofmann
  3. Edward Cutrell
  4. Simone Stumpf
  5. Lida Theodorou
  6. Luisa M. Zintgraf
  7. Daniela Massiceti
  8. John F. Bronskill

In Representation & Self-Supervised Learning

  1. Katja Hofmann
  2. Simone Stumpf
  3. Luisa M. Zintgraf
  4. Daniela Massiceti
  5. John F. Bronskill

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