Semantic Hashing

Semantic hashing aims to efficiently represent data, such as images, as compact binary codes that preserve semantic similarity, enabling fast similarity search without relying on computationally expensive comparisons. Current research focuses on improving the discriminative power of these hash codes, often employing deep learning architectures and leveraging techniques like contrastive learning and knowledge distillation to enhance performance and efficiency. This work is driven by the need for scalable and fast similarity search in large-scale datasets, with applications ranging from image retrieval to information retrieval.