Related papers: Trainable Class Prototypes for Few-Shot Learning

Trainable Class Prototypes for Few-Shot Learning

URL: http://arxiv.org/abs/2106.10846v1
Date: Mon, 21 Jun 2021 04:19:56 GMT
Title: Trainable Class Prototypes for Few-Shot Learning
Authors: Jianyi Li and Guizhong Liu
Abstract summary: We propose the trainable prototypes for distance measure instead of the artificial ones within the meta-training and task-training framework. Also to avoid the disadvantages that the episodic meta-training brought, we adopt non-episodic meta-training based on 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 dataset.
Score: 5.481942307939029
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Metric learning is a widely used method for few shot learning in which the quality of prototypes plays a key role in the algorithm. In this paper we propose the trainable prototypes for distance measure instead of the artificial ones within the meta-training and task-training framework. Also to avoid the disadvantages that the episodic meta-training brought, we adopt non-episodic meta-training based on self-supervised learning. Overall we solve the few-shot tasks in two phases: meta-training a transferable feature extractor via self-supervised learning and training the prototypes for metric classification. In addition, the simple attention mechanism is used in both meta-training and task-training. Our method achieves state-of-the-art performance in a variety of established few-shot tasks on the standard few-shot visual classification dataset, with about 20% increase compared to the available unsupervised few-shot learning methods.

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