Disturbance Consistent Self-Ensembling for Semi-Supervised Hashing

Recently, deep semi-supervised hashing methods have attracted increasing attention, where the visual similarity of unlabeled data is usually adopted to guide the hash codes learning. However, samples with similar appearance may come from different categories, making their hash codes similar will lead to sub-optimal retrieval results. In this paper, we propose a novel Disturbance Consistent Self-Ensembling (DCSE) method to alleviate the drawback of visual similarity constraint. Specially, DCSE forms consensus hash codes for the same sample under different augmentations. These ensemble hash codes can capture the discriminative characteristics of a sample. Therefore, as more augmented data is involved, more ensemble hash codes in one category can become similar gradually. Then, we design a disturbance consistent loss to learn the discriminative hash codes by minimizing the distance between outputs of the hash layer and ensemble hash codes. Extensive experiments show that our proposed approach significantly outperforms state-of-the-art semi-supervised hashing methods.