A Novel Few-Shot Learning Approach for Improving Model Generalization with Minimal Data
Abstract
Traditional machine learning models require extensive labeled data for effective training, which is often impractical in real-world scenarios. This paper introduces a novel few-shot learning framework that enhances model generalization using meta-learning techniques and contrastive learning. We compare our approach with traditional supervised learning and transfer learning methods, evaluating performance on image classification, text processing, and medical diagnosis datasets. The findings demonstrate that our proposed method significantly reduces data dependency while maintaining high predictive accuracy.
References
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