Class Forge: Boosting Feature Encoder for Few-Shot Learning with Synthesized Classes

Rui-Qi Wang, Xu-Yao Zhang, Cheng-Lin Liu

Abstract: Few-shot Learning (FSL) aims to gain classification ability on novel classes with only a few labeled samples. Previous works explore meta-learning, metric learning, and graph based methods. Though data augmentation is important to enhance the generalizability of neural networks, it is not well exploited in the field of FSL. We investigate the augmentation in FSL and propose Class Forge to synthesize forged classes that help to learn an encoder with better generalization to novel classes. Specifically, Class Forge divides given base visual classes into parts and combines these parts to synthesize forged visual classes. Training with the additional forged classes forces the encoder to learn richer features that can embed different parts, so as to boost the generalization to novel classes. Intrinsically, Class Forge is a "class augmentation" method that provides a simple yet effective way to synthesize classes, other than synthesizing samples of given classes in previous works. Extensive experiments show that Class Forge yields consistent performance gain on different datasets for FSL. And the ablation studies validate that features learned with Class Forge demonstrate better generalization ability.