Densified Winner Take All (WTA) Hashing for Sparse Datasets

Beidi Chen, Anshumali Shrivastava
2018 Conference on 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
more » ... se 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.
dblp:conf/uai/ChenS18 fatcat:cd7zn36bzfgyjjhkwrj56ivcpi