Unsupervised few-shot image classification via one-vs-all contrastive learning

Human beings innately possess the ability to perceive novel concepts from only a few samples. As a setting to imitate the learned ability of human beings, few-shot image classification (FSIC) has recently aroused a research boom. FSIC aims to distinguish the novel class when given scarce samples. However, most of the existing few-shot methods build on the assumption that an adequate labeled dataset is provided in the source domain, ie, the base class dataset. Due to the expensive burden of labeled samples in the base class, this assumption may not be practical in a real-world application. To solve this labeled burden, in the paper, we propose a novel unsupervised few-shot image classification via One-vs-All contrastive learning. In this approach, to generate positive pairs in each instance, we first adopt a data augmentation technique to build the instance invariance. With the positive pairs, a neural projection with only a fully connected layer is then applied to maintain the structure consistent with the corresponding features of positive pairs. Finally, the One-Vs-All (OVA) contrastive learning is devised to pull one positive pair close while pushing all negative pairs away in a minibatch. By doing this OVA contrastive learning, the model can effectively acquire the discriminative feature and improve the generalization ability to recognize the novel class. We also further develop a theory of the generalized upper bound of the model for the OVA contrastive loss. Our experimental analyses suggest that the proposed approach achieves better performance compared to most existing few-shot methods, and various modules in the approach demonstrate their utility by conducting ablation studies.