Robust Mutual Learning Hashing
Lei Wu, Yang Fang, Hefei Ling, Jiazhong Chen, Ping Li · 2019
With the advances in deep learning, deep hashing methods have achieved promising results in recent years. However, tackling the distribution gap between train data and test data still remains unsolved. In this paper, motivated by Spatial Transformer Networks (STN) and mutual learning, we propose a novel robust hashing method (RMLH) for effective image retrieval. Specifically, the network learns more flexible transformation and makes itself generalize better to test data by plugging STN module. Then the mutual learning strategy is introduced to stabilize the training process. Furthermore, we relax the binary variables into continuous variables to avoid introducing any auxiliary variable. Finally, the experimental results show that our method has achieved the state-of-the-art performance on benchmark datasets.