Contrastive Clustering

نویسندگان

چکیده

In this paper, we propose an online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, positive negative instance pairs are constructed through data augmentations then projected into feature space. Therein, learning respectively conducted in row column space by maximizing similarities of while minimizing those ones. Our key observation is that rows matrix could regarded as soft labels instances, accordingly columns further cluster representations. By simultaneously optimizing loss, model jointly learns representations assignments end-to-end manner. Besides, proposed timely compute assignment each individual, even when presented streams. Extensive experimental results show CC remarkably outperforms 17 competitive methods on six challenging image benchmarks. particular, achieves NMI 0.705 (0.431) CIFAR-10 (CIFAR-100) up to 19% (39%) performance improvement compared with best baseline. The code available at https://github.com/XLearning-SCU/2021-AAAI-CC.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2021

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v35i10.17037