Abstract
The clustering of unlabeled raw images is a daunting task, which has recently been approached with some success by deep learning methods. Here we propose an unsupervised clustering framework, which learns a deep neural network in an end-to-end fashion, providing direct cluster assignments of images without additional processing. Multi-Modal Deep Clustering (MMDC), trains a deep network to align its image embeddings with target points sampled from a Gaussian Mixture Model distribution. The cluster assignments are then determined by mixture component association of image embeddings. Simultaneously, the same deep network is trained to solve an additional self-supervised task of predicting image rotations. This pushes the network to learn more meaningful image representations that facilitate a better clustering. Experimental results show that MMDC achieves or exceeds state-of-the-art performance on six challenging benchmarks. On natural image datasets we improve on previous results with significant margins of up to 20% absolute accuracy points, yielding an accuracy of 82% on CIFAR-10, 45% on CIFAR-100 and 69% on STL-10.
Original language | English |
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Title of host publication | Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4728-4735 |
Number of pages | 8 |
ISBN (Electronic) | 9781728188089 |
DOIs | |
State | Published - 2020 |
Event | 25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy Duration: 10 Jan 2021 → 15 Jan 2021 |
Publication series
Name | Proceedings - International Conference on Pattern Recognition |
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ISSN (Print) | 1051-4651 |
Conference
Conference | 25th International Conference on Pattern Recognition, ICPR 2020 |
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Country/Territory | Italy |
City | Virtual, Milan |
Period | 10/01/21 → 15/01/21 |
Bibliographical note
Publisher Copyright:© 2020 IEEE