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Discrete Wasserstein Autoencoders For Document Retrieval
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Discrete Wasserstein Autoencoders For Document Retrieval
Learning to hash via generative models has become 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 d
Learning to hash via generative models has become 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 d
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