NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing

Understand

Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems.

• While fairly successful, previous techniques generally require two-stage training, and the binary constraints are handled ad-hoc.
• In this paper, we present an end-to-end Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as Bernoulli latent variables.
• A neural variational inference framework is proposed for training, where gradients are directly back-propagated through the discrete latent variable to optimize the hash function.