Knowledge transduction for cross-domain few-shot learning

Abstract

Cross-Domain Few-Shot Learning (CDFSL) aims to classify new categories from new domains with few samples. It confronts a greater domain shift than Few-Shot Learning (FSL). Based on the transfer learning framework, we propose a Knowledge Transduction method (KT) to alleviate domain shift and achieve few-shot recognition. First, a feature adaptation module based on feed-forward attention is constructed to learn domain-adapted features. The feature adaptation module weakens domain shift by transducing knowledge from an auxiliary dataset to the new dataset. Second, a feature transduction module based on deep sparse representation is developed to gather class semantics from limited support images. The feature transduction module transduces knowledge from support images to query images for few-shot recognition. In addition, a stochastic image augmentation method is proposed for FSL to train a more generalized model through consistency representation learning. Our method achieves competitive accuracy on four CDFSL datasets and four FSL datasets compared to state-of-the-art methods. The source code is available at https://github.com/XDUpfLi/KT.