{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-modal-deep-clustering-unsupervised","title":"Multi-Modal Deep Clustering: Unsupervised Partitioning of Images","arxiv_id":"1912.02678","date":"2019-12-05","proceeding":null,"authors":["Guy Shiran","Daphna Weinshall"],"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.","url_abs":"https://arxiv.org/abs/1912.02678v3","url_pdf":"https://arxiv.org/pdf/1912.02678v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-modal-deep-clustering-unsupervised","repo_url":"https://github.com/guysrn/mmdc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"deep-clustering","task_name":"Deep Clustering"},{"task_slug":"image-clustering","task_name":"Image Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-clustering-on-cifar-10","task":"Image Clustering","dataset":"CIFAR-10","model":"MMDC","rank_in_archive_order":26,"of":40,"metrics":{"Accuracy":"0.820","Backbone":"ResNet18","NMI":"0.703"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-cifar-100","task":"Image Clustering","dataset":"CIFAR-100","model":"MMDC","rank_in_archive_order":18,"of":30,"metrics":{"Accuracy":"0.446","NMI":"0.418"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-imagenet-10","task":"Image Clustering","dataset":"ImageNet-10","model":"MMDC","rank_in_archive_order":12,"of":18,"metrics":{"Accuracy":"0.811","NMI":"0.719"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-stl-10","task":"Image Clustering","dataset":"STL-10","model":"MMDC","rank_in_archive_order":20,"of":29,"metrics":{"Accuracy":"0.694","Backbone":"ResNet18","NMI":"0.593"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-tiny-imagenet","task":"Image Clustering","dataset":"Tiny-ImageNet","model":"MMDC","rank_in_archive_order":8,"of":14,"metrics":{"Accuracy":"0.119","NMI":"0.274"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1912.02678","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}