Discrete Wasserstein Autoencoders for Document Retrieval

Abstract

Learning to hash via generative models has became a promising paradigm for fast similarity search in document retrieval. The binary hash codes are treated as Bernoulli latent variables when training a variational autoencoder (VAE). However, the prior of discrete distribution (i.e., Bernoulli distribution) is short of some structure regularization to generate more effi-cient binary codes. In this paper, we present an end-to-end Wasserstein Autoencoder (WAE) for text hashing to avoid in-differentiable operators in the reparameterization trick, where the latent variables can be imposed to any discrete priors we can sample by using adversarial learning. Moreover, we can generate more efficient discrete codes by imposing a structural constraint on priors like the bit balance constraint. Our experiments show that the proposed model is competitive to the state of the art methods on both unsupervised and supervised scenarios.