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DCRP: Class-Aware Feature Diffusion Constraint and Reliable Pseudo-Labeling for Imbalanced Semi-Supervised Learning | IEEE Journals & Magazine | IEEE Xplore
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DCRP: Class-Aware Feature Diffusion Constraint and Reliable Pseudo-Labeling for Imbalanced Semi-Supervised Learning


Abstract:

Despite the astounding progress made in semi-supervised learning (SSL) and imbalanced supervised learning (ISL), there has been little attention devoted to the research o...Show More

Abstract:

Despite the astounding progress made in semi-supervised learning (SSL) and imbalanced supervised learning (ISL), there has been little attention devoted to the research of imbalanced semi-supervised learning (ISSL). The “Matthew effect”, a phenomenon where a disparity in data representation becomes more severe in a class-imbalanced dataset during training, could be amplified in a semi-supervised setting. In this study, we addressed two key challenges in ISSL: maintaining the reliability of pseudo-labels and ensuring a balanced representation of features. Specifically, we propose a class-aware feature-diffusion constraint and reliable pseudo-labeling (DCRP) framework to address these issues. In the DCRP, we counteract the overconfidence problem of softmax by adding an extra class to the typical K class problem without the need for additional parameters. Moreover, we introduced a flexible class-aware feature diffusion constraint in the feature extractor, promoting a more balanced feature diversity. Experimental validations on various datasets, such as CIFAR10-LT, CIFAR100-LT, SVHN-LT, and Small ImageNet-127, demonstrated consistent improvements in accuracy with our DCRP method. In particular, we achieved a steady improvement in accuracy of approximately 1% under the newly published ACR prototype across most settings.
Published in: IEEE Transactions on Multimedia ( Volume: 26)
Page(s): 7146 - 7159
Date of Publication: 31 January 2024

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