1. Deep debiased contrastive hashing.
- Author
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Wei, Rukai, Liu, Yu, Song, Jingkuan, Xie, Yanzhao, and Zhou, Ke
- Subjects
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ARTIFICIAL neural networks , *EXPECTATION-maximization algorithms , *IMAGE retrieval , *DEEP learning - Abstract
• A novel debiased contrastive hashing method for accurate image retrieval. • Introducing neighborhood discovery to alleviate the bias problem caused by false negatives. • We bridge contrastive hashing and the EM algorithm to provide some insights. • Our proposed method outperforms state-of-the-art unsupervised hashing methods. Hashing has achieved great success in multimedia retrieval due to its high computing efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have achieved decent retrieval performance in label-free scenarios by learning distortion-invariant representations with Siamese networks. Their learning principle, i.e., instance discrimination , maximizes the correlation between self-augmented views and treats all others as negative samples. However, it may learn with false negative samples that are naturally similar, resulting in biased hash learning. To bridge this flaw, we reveal the between-instance similarity of naturally similar samples by exploring the latent structure of the training data. As a result, we propose the D eep D ebiased C ontrastive H ashing (DDCH) algorithm, using the neighborhood discovery module to explore the intrinsic similarity relationship that can help contrastive hashing reduce false negatives for superior discriminatory ability. Furthermore, we elucidate the rationale for incorporating the module into the contrastive hashing framework and explain our hashing process from an Expectation-Maximization (EM) perspective. Extensive experimental results on three benchmark image datasets demonstrate that DDCH significantly outperforms the state-of-the-art unsupervised hashing methods for image retrieval. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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