Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning

• URL: http://arxiv.org/abs/2004.00251v3
• Date: Tue, 4 Aug 2020 08:37:35 GMT
• Title: Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning
• Authors: Jin-Woo Seo, Hong-Gyu Jung, Seong-Whan Lee
• Abstract summary: Few-shot learning aims to classify unseen classes with a few training examples. We propose self-augmentation that consolidates self-mix and self-distillation. We present a local learner representation to further exploit a few training examples for unseen classes.
• Score: 21.3564383157159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot learning aims to classify unseen classes with a few training examples. While recent works have shown that standard mini-batch training with a carefully designed training strategy can improve generalization ability for unseen classes, well-known problems in deep networks such as memorizing training statistics have been less explored for few-shot learning. To tackle this issue, we propose self-augmentation that consolidates self-mix and self-distillation. Specifically, we exploit a regional dropout technique called self-mix, in which a patch of an image is substituted into other values in the same image. Then, we employ a backbone network that has auxiliary branches with its own classifier to enforce knowledge sharing. Lastly, we present a local representation learner to further exploit a few training examples for unseen classes. Experimental results show that the proposed method outperforms the state-of-the-art methods for prevalent few-shot benchmarks and improves the generalization ability.

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