Deep Semantic Hashing with Structure-Semantic Disagreement Correction via Hyperbolic Metric Learning

Semantic hashing is a crucial component of content based search and retrieval systems. To achieve an effective semantic hashing for images, it is essential to map them to hash space in a way that preserves the semantic information. Most state-of-the-art deep semantic hashing approaches do not fully take into account the structural information and the inherent hierarchy in the dataset. Also, the distribution of hash codes is primarily driven by semantic information that comes from the supervision labels. We propose a semantic hashing framework which utilizes the hyperbolic metric learning to learn the structural and hierarchical information. This information is leveraged in the form of proxy labels for training the hashing network with the proposed novel Structure-Semantic Disagreement (SSD) loss. SSD enforces the model to learn to hash with semantic as well as structural information, leading to more robust and uniformly distributed hash codes. Tests on multiple public domain datasets establish the effectiveness of the proposed approach. Moreover, the developed SSD loss can also be applied to other classification models to improve the representation by enforcing the model to use the structure information more effectively.