Learning task-specific discriminative embeddings for few-shot image classification

Recently, few-shot learning has attracted more and more attention. Generally, the fine-tuning-based few shot learning framework contains two stages: i) In the pre-training stage, using base data to train the feature extractor; ii) In the meta-testing stage, using a well-trained feature extractor to extract embedding features of novel data and designing a base learner to predict the labels. Due to the diverse categories of base and novel data, it is challenging for the feature extractor trained in the pre-training stage to adapt to novel data, which will result in an embedding-mismatch problem. This paper proposes Task-specific Discriminative Embeddings for Few-shot Learning (TDE-FSL) to solve the embedding-mismatch problem. Specifically, we embed the dictionary learning method into the few-shot learning framework to map the feature embeddings to a more discriminative subspace to adapt to the specific task. Moreover, we extend the self-training framework to our approach to fully utilize the unlabeled data. Finally, we evaluate the TDE-FSL on five benchmark image datasets, such as mini-Imagenet, tiered-Imagenet, CIFAR-FS, FC100, and CUB dataset. The experimental results show that the performance of our proposed TDE-FSL achieves a significant improvement.(c) 2022 Elsevier B.V. All rights reserved.