Augmenting Few-Shot Learning with Supervised Contrastive Learning

Few-shot learning deals with a small amount of data which incurs insufficient performance with conventional cross-entropy loss. We propose a pretraining approach for few-shot learning scenarios. That is, considering that the feature extractor quality is a critical factor in few-shot learning, we augment the feature extractor using a contrastive learning technique. It is reported that supervised contrastive learning applied to base class training in transductive few-shot training pipeline leads to improved results, outperforming the state-of-the-art methods on Mini-ImageNet and CUB. Furthermore, our experiment shows that a much larger dataset is needed to retain few-shot classification accuracy when domain-shift degradation exists, and if our method is applied, the need for a large dataset is eliminated. The accuracy gain can be translated to a runtime reduction of $3.87\times $ in a resource-constrained environment.