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    Cross-Modal Deep Variational Hashing by Liong, Venice Erin, Lu, Jiwen, Zhou, Jie, Tan, Yap Peng

    Published 2017
    “…Unlike most existing cross-modal hashing methods which learn a single pair of projections to map each example into a binary vector, we design a deep fusion neural network to learn non-linear transformations from image-text input pairs, such that a unified binary code is achieved in a discrete and discriminative manner using a classification-based hinge-loss criterion. We then design modality-specific neural networks in a probabilistic manner such that we model a latent variable to be close as possible from the inferred binary codes, at the same time approximated by a posterior distribution regularized by a known prior, which is suitable for out-of-sample extension. …”
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    Conference Paper
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