Learning to hash: a comprehensive survey of deep learning-based hashing methods
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10115-022-01734-0.pdf
Reference62 articles.
1. Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53:5455–5516
2. Çakir F, He K, Bargal SA, Sclaroff S (2019) Hashing with mutual information. IEEE Trans Pattern Anal Mach Intell 41(10):2424–2437
3. Cao Y, Liu B, Long M, Wang J (2018) HashGAN: deep learning to hash with pair conditional Wasserstein GAN. In: IEEE conference on computer vision and pattern recognition, CVPR, Salt Lake City, UT, USA, pp 1287–1296
4. Cao Y, Long M, Liu B , Wang J (2018) Deep Cauchy hashing for hamming space retrieval. In: Conference on computer vision and pattern recognition
5. Cao Y, Long M, Wang J, Liu S (2017) Deep visual-semantic quantization for efficient image retrieval. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 916–925
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