Few-Shot Image Classification via Contrastive Self-Supervised Learning

• URL: http://arxiv.org/abs/2008.09942v1
• Date: Sun, 23 Aug 2020 02:24:31 GMT
• Title: Few-Shot Image Classification via Contrastive Self-Supervised Learning
• Authors: Jianyi Li and Guizhong Liu
• Abstract summary: We propose a new paradigm of unsupervised few-shot learning to repair the deficiencies. We solve the few-shot tasks in two phases: meta-training a transferable feature extractor via contrastive self-supervised learning. Our method achieves state of-the-art performance in a variety of established few-shot tasks on the standard few-shot visual classification datasets.
• Score: 5.878021051195956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most previous few-shot learning algorithms are based on meta-training with fake few-shot tasks as training samples, where large labeled base classes are required. The trained model is also limited by the type of tasks. In this paper we propose a new paradigm of unsupervised few-shot learning to repair the deficiencies. We solve the few-shot tasks in two phases: meta-training a transferable feature extractor via contrastive self-supervised learning and training a classifier using graph aggregation, self-distillation and manifold augmentation. Once meta-trained, the model can be used in any type of tasks with a task-dependent classifier training. Our method achieves state of-the-art performance in a variety of established few-shot tasks on the standard few-shot visual classification datasets, with an 8- 28% increase compared to the available unsupervised few-shot learning methods.

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