Contrastive Hashing

Contrastive hashing aims to efficiently represent data using compact binary codes, enabling fast similarity search in large-scale datasets. Current research focuses on improving the accuracy and efficiency of these hash codes, often leveraging deep learning architectures and contrastive learning techniques to learn effective representations, incorporating graph structures or multi-modal information to capture richer relationships between data points. This approach has significant implications for various applications, including drug discovery, video retrieval, and remote sensing, where rapid and memory-efficient similarity search is crucial.