Balance the Loss: Improving Deep Hash via Loss Weighting and Semantic Preserving

Learning to hash is widely used in approximate nearest-neighbor (ANN) search. However, traditional hash learning methods, which split the hashing into two parts: feature extraction and hash function learning, usually result in a low retrieval accuracy. Although existing deep learning based hashing methods can improve hashing quality by coupling feature learning and hash encoding, they are always affected by the positive-negative sample imbalance problem. It often deteriorates the performance of the generated hash code. In this paper, we propose an end-to-end deep hashing framework, in which a weighted pairwise loss function is employed to alleviate sample imbalance problem. The loss generated by the positive pairs and negative pairs are given different weights automatically. Moreover, we integrate a classification network into the hashing framework, which can preserve the semantic information by making sure the generated hash codes are also optimal for classification. Comparison experiments are conducted on two benchmark datasets to demonstrate the performance of our proposed approach.