CVOCSemRPL: Class-Variance Optimized Clustering, Semantic Information Injection and Restricted Pseudo Labeling based Improved Semi-Supervised Few-Shot Learning

• URL: http://arxiv.org/abs/2501.14401v1
• Date: Fri, 24 Jan 2025 11:14:35 GMT
• Title: CVOCSemRPL: Class-Variance Optimized Clustering, Semantic Information Injection and Restricted Pseudo Labeling based Improved Semi-Supervised Few-Shot Learning
• Authors: Rhythm Baghel, Souvik Maji, Pratik Mazumder,
• Abstract summary: Unlabeled samples are generally cheaper to obtain and can be used to improve the few-shot learning performance of the model. We propose an approach for semi-supervised few-shot learning that performs a class-variance optimized clustering. We experimentally demonstrate that our proposed approach significantly outperforms recent state-of-the-art methods on the benchmark datasets.
• Score: 4.3149314441871205
• License:
• Abstract: Few-shot learning has been extensively explored to address problems where the amount of labeled samples is very limited for some classes. In the semi-supervised few-shot learning setting, substantial quantities of unlabeled samples are available. Such unlabeled samples are generally cheaper to obtain and can be used to improve the few-shot learning performance of the model. Some of the recent methods for this setting rely on clustering to generate pseudo-labels for the unlabeled samples. Since the quality of the representation learned by the model heavily influences the effectiveness of clustering, this might also lead to incorrect labeling of the unlabeled samples and consequently lead to a drop in the few-shot learning performance. We propose an approach for semi-supervised few-shot learning that performs a class-variance optimized clustering in order to improve the effectiveness of clustering the labeled and unlabeled samples in this setting. It also optimizes the clustering-based pseudo-labeling process using a restricted pseudo-labeling approach and performs semantic information injection in order to improve the semi-supervised few-shot learning performance of the model. We experimentally demonstrate that our proposed approach significantly outperforms recent state-of-the-art methods on the benchmark datasets.

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