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SV-Learner: Support-Vector Contrastive Learning for Robust Learning With Noisy Labels | IEEE Journals & Magazine | IEEE Xplore

SV-Learner: Support-Vector Contrastive Learning for Robust Learning With Noisy Labels


Abstract:

Noisy-label data inevitably gives rise to confusion in various perception applications. In this work, we revisit the theory of support vector machines (SVMs) which mines ...Show More

Abstract:

Noisy-label data inevitably gives rise to confusion in various perception applications. In this work, we revisit the theory of support vector machines (SVMs) which mines support vectors to build the maximum-margin hyperplane for robust classification, and propose a robust-to-noise deep learning framework, SV-Learner, including the Support Vector Contrastive Learning (SVCL) and Support Vector-based Noise Screening (SVNS). The SV-Learner mines support vectors to solve the learning problem with noisy labels (LNL) reliably. SVCL adopts support vectors as positive and negative samples, driving robust contrastive learning to enlarge the feature distribution margin for learning convergent feature distributions. SVNS uses support vectors with valid labels to assist in screening noisy ones from confusable samples for reliable clean-noisy sample screening. Finally, Semi-Supervised classification is performed to realize the recognition of noisy samples. Extensive experiments are evaluated on CIFAR-10, CIFAR-100, Clothing1M, and Webvision datasets, and results demonstrate the effectiveness of our proposed approach.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 36, Issue: 10, October 2024)
Page(s): 5409 - 5422
Date of Publication: 09 April 2024

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