Adaptive fine-tuning strategy for few-shot learning

Few-shot classification aims to recognize innovative classes that lack enough labeled examples. Existing few-shot learning methods train a model on the source dataset and transfer knowledge to the target dataset. However, the trained model cannot generalize well to unseen tasks, especially when there are domain shifts. To address the aforementioned limitation, we propose an adaptive fine-tuning strategy to improve the generalizability of the pre-trained model. Our core idea is adaptively transferring target-task-specific knowledge to different target tasks. Specifically, we perform an evolutionary search on the task-specific validation dataset similar to the target dataset. Class hierarchy structure is introduced to seek the closest validation super class of the novel class. Meanwhile, our work takes the evolutionary searched model instead of the pretrained network serving as the initialization of fine-tuning. Extensive experiments proceeded and demonstrate the effectiveness of our adaptive fine-tuning strategy. Furthermore, our proposed method can be easily combined with meta-learning and transfer learning methods to improve their generalizability.