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Creators/Authors contains: "Beidi Chen, Anshumali Shrivastava"

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  1. (, Uncertainty in artificial intelligence)
    WTA (Winner Take All) hashing has been successfully applied in many large-scale vision applications. This hashing scheme was tailored to take advantage of the comparative reasoning (or order based information), which showed significant accuracy improvements. In this paper, we identify a subtle issue with WTA, which grows with the sparsity of the datasets. This issue limits the discriminative power of WTA. We then propose a solution to this problem based on the idea of Densification which makes use of 2-universal hash functions in a novel way. Our experiments show that Densified WTA Hashing outperforms Vanilla WTA Hashing both in image retrieval and classification tasks consistently and significantly 
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    Accepted Manuscript Full Text Available