Few-Shot Learning with Complex-valued Neural Networks

Feature representation is fundamental and attracts much attention in few-shot learning. Convolutional neural networks (CNNs) are among the best feature extractors so far in this field, which are successfully combined with metric learning, leading to the state-of-the-art performance. However, the subtle difference among inter-class samples challenges existing CNN based methods, which only use real-valued CNNs that fail to extract more detailed information. In this paper, we introduce complex metric module (CMM) into metric learning, aiming to better measure the inter- and intra-class relations based on both amplitude and phase information. Specifically, building upon the recent episodic training mechanism, our CMM can enhance the representation capacity by extracting robust complex-valued features to facilitate modeling subtle relationships among samples, which can enhance the performance of the few-shot classification task when only few samples are available. Moreover, we introduce a new transductive method into CMM, by considering not only query and support but also query and query relationships to predict classes of unlabeled samples. Experiments on two benchmark datasets show that the proposed CMM significantly improves the performance over other approaches and achieves the state-of-the-art results.