Self-Supervised Curriculum-based Class Incremental Learning

Self-Supervised Curriculum-based Class Incremental Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Self-Supervised Curriculum-based Class Incremental Learning Kartik Thakral, Surbhi Mittal, Utkarsh Uppal, Bharat Giddwani, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6166408/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Class-incremental learning, a sub-field of continual learning, faces catastrophic forgetting, where models forget previous tasks while learning new ones. Existing solutions fall into expansion-based, memory-based, and regularization-based approaches, with limited focus on the latter despite its deployability and efficiency. This paper introduces Self-Supervised Curriculum-based Class Incremental Learning (S 2 C 2 IL), a novel regularization-based algorithm that improves class-incremental performance without external memory or network expansion. S 2 C 2 IL leverages self-supervised learning to extract rich feature representations using a new pretext task based on stochastic label augmentation instead of image augmentation. To prevent pretext task-specific knowledge transfer, the final section of the pre-trained network is excluded in feature transfer. For downstream tasks, a curriculum strategy periodically adjusts the standard deviation of a filter fused with the network. Evaluated on split-CIFAR10, split-CIFAR100, split-SVHN, and split-TinyImageNet, S 2 C 2 IL achieves state-of-the-art results, outperforming existing regularization-based and memory-based class-incremental algorithms. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Supplementary Files supplementaryscireports.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 24 Jun, 2025 Editor invited by journal 25 Mar, 2025 Submission checks completed at journal 22 Mar, 2025 First submitted to journal 05 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6166408","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":476954569,"identity":"54f548a2-90eb-4d03-8560-56b9fd14fbc8","order_by":0,"name":"Kartik Thakral","email":"","orcid":"","institution":"IIT Jodhpur","correspondingAuthor":false,"prefix":"","firstName":"Kartik","middleName":"","lastName":"Thakral","suffix":""},{"id":476954570,"identity":"f769e7d0-ec11-4e61-8a70-2b263e22d057","order_by":1,"name":"Surbhi Mittal","email":"","orcid":"","institution":"IIT 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