Deep Domain Knowledge Distillation for Person Re-identification

Abstract

Learning generic and robust representations with data from multiple domains is a big challenge in Person ReID. In this paper, we propose an end-to-end framework called Deep Domain Knowledge Distillation (\(D^2KD\)) for leaning more generic and robust features with Convolutional Neural Networks (CNNs). Domain-specific knowledge learned by the auxiliary network is transferred to the domain-free subnetwork and guides the optimization of the feature extractor. While person identity information is transferred to the auxiliary network to further accurately identify domain classes. In the test period, just with a single base model as the feature extractor, we improve the Rank-1 and mAP by a clear margin. Experiments on Market-1501, CUHK03 and DukeMTMC-reID demonstrate the effectiveness of our method.