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

2025-01-24

AI Summary

In semi-supervised few-shot learning, erroneous pseudo-label propagation degrades clustering quality and model performance. To address this, we propose a unified framework integrating class-variance-optimized clustering, semantic embedding enhancement, and confidence-gated pseudo-labeling. First, metric learning constructs a discriminative feature space; second, hierarchical clustering optimization improves intra-class compactness and inter-class separability; third, semantic constraints guide pseudo-label generation to suppress error accumulation from low-confidence predictions. Evaluated on multiple standard benchmarks, our method consistently outperforms existing state-of-the-art approaches, achieving average improvements of 3.2–5.8 percentage points in few-shot classification accuracy. These results demonstrate the framework’s robustness and effectiveness in leveraging unlabeled data under extremely limited labeling budgets.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• Few-shot Learning
• Semi-supervised Learning
• Model Accuracy

Innovation

• Semi-supervised Learning
• Optimized Clustering
• Enhanced Semantic Understanding