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Deep Semantic Hashing with Multi-Adversarial Training

Published: 17 October 2018 Publication History

Abstract

With the amount of data has been rapidly growing over recent decades, binary hashing has become an attractive approach for fast search over large databases, in which the high-dimensional data such as image, video or text is mapped into a low-dimensional binary code. Searching in this hamming space is extremely efficient which is independent of the data size. A lot of methods have been proposed to learn this binary mapping. However, to make the binary codes conserves the input information, previous works mostly resort to mean squared error, which is prone to lose a lot of input information [11]. On the other hand, most of the previous works adopt the norm constraint or approximation on the hidden representation to make it as close as possible to binary, but the norm constraint is too strict that harms the expressiveness and flexibility of the code.
In this paper, to generate desirable binary codes, we introduce two adversarial training procedures to the hashing process. We replace the L2 reconstruction error with an adversarial training process to make the codes reserve its input information, and we apply another adversarial learning discriminator on the hidden codes to make it proximate to binary. With the adversarial training process, the generated codes are getting close to binary while also conserves the input information. We conduct comprehensive experiments on both supervised and unsupervised hashing applications and achieves a new state of the arts result on many image hashing benchmarks.

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Cited By

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  • (2021)Deep Self-Adaptive Hashing for Image RetrievalProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482247(1028-1037)Online publication date: 26-Oct-2021
  • (2021)LASH: Large-scale Academic Deep Semantic HashingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3109433(1-1)Online publication date: 2021

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cover image ACM Conferences
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
October 2018
2362 pages
ISBN:9781450360142
DOI:10.1145/3269206
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Published: 17 October 2018

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Author Tags

  1. auto-encoder
  2. generative adversarial network
  3. semantic hashing
  4. unsupervised learning

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  • Research-article

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  • National Science Foundation of China
  • Ant Financial Services Group
  • National Laboratory of Pattern Recognition

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2021)Deep Self-Adaptive Hashing for Image RetrievalProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482247(1028-1037)Online publication date: 26-Oct-2021
  • (2021)LASH: Large-scale Academic Deep Semantic HashingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3109433(1-1)Online publication date: 2021

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