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We present a supervised semantic hashing method which uses a variational autoencoder to represent each database image sample as a product Bernoulli distribution. We show that the probability parameters approach extreme values during training, allowing the
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A New Variational Method For Deep Supervised Semantic Image Hashing
We present a supervised semantic hashing method which uses a variational autoencoder to represent each database image sample as a product Bernoulli distribution. We show that the probability parameters approach extreme values during training, allowing the