Deep Hashing

Deep hashing is a technique that efficiently encodes high-dimensional data, such as images, into compact binary codes for fast similarity search. Current research emphasizes improving the accuracy and robustness of these codes, focusing on novel loss functions (e.g., incorporating distributional matching or hybrid proxy-pair approaches), architectures like autoencoders and twin-bottleneck networks, and adversarial training methods to enhance resilience against attacks. This field is significant for its potential to accelerate large-scale image retrieval and other similarity search tasks across diverse applications, including those involving fine-grained images, multi-modal data, and historical documents.