{"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/ipcl-iterative-pseudo-supervised-contrastive","title":"IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation","arxiv_id":null,"date":"2024-03-18","proceeding":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024 3","authors":["Sonal Kumar; Anirudh Phukan","Arijit Sur"],"abstract":"Self-supervised learning with a contrastive batch approach has become a powerful tool for representation learning in computer vision. The performance of downstream tasks is proportional to the quality of visual features learned while self-supervised pre-training. The existing contrastive batch approaches heavily depend on data augmentation to learn latent information from unlabelled datasets. We argue that introducing the dataset’s intra-class variation in a contrastive batch approach improves visual representation quality further. In this paper, we propose a novel self-supervised learning approach named Iterative Pseudo-supervised Contrastive Learning (IPCL), which utilizes a balanced combination of image augmentations and pseudo-class information to improve the visual representation iteratively. Experimental results illustrate that our proposed method surpasses the baseline self-supervised method with the batch contrastive approach. It improves the visual representation quality over multiple datasets, leading to better performance on the downstream unsupervised image classification task.","url_abs":"https://doi.org/10.1109/ICASSP48485.2024.10447607","url_pdf":"https://doi.org/10.1109/ICASSP48485.2024.10447607","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":"ipcl-iterative-pseudo-supervised-contrastive","repo_url":"https://github.com/SonalKumar95/IPCL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-clustering","task_name":"Image Clustering"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"self-supervised-image-classification","task_name":"Self-Supervised Image Classification"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"unsupervised-image-classification","task_name":"Unsupervised Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/contrastive-learning-on-cifar-10","task":"Contrastive Learning","dataset":"CIFAR-10","model":"IPCL (ResNet18)","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (Top-1)":"84.77"},"uses_additional_data":false},{"leaderboard":"/sota/contrastive-learning-on-stl-10","task":"Contrastive Learning","dataset":"STL-10","model":"IPCL (ResNet18)","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (Top-1)":"85.55"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-image-classification-on-cifar-10","task":"Unsupervised Image Classification","dataset":"CIFAR-10","model":"IPCL (ResNet18)","rank_in_archive_order":9,"of":9,"metrics":{"Accuracy ":"88.81"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-image-classification-on-stl-10","task":"Unsupervised Image Classification","dataset":"STL-10","model":"IPCL (ResNet18)","rank_in_archive_order":9,"of":9,"metrics":{"Accuracy ":"80.91"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}