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Few-shot learning addresses the challenge of training effective machine learning models with limited labeled data. Current research focuses on adapting existing architectures, such as vision transformers and deep kernel Gaussian processes, through techniques like prototype-based soft labels, test-time learning, and meta-learning strategies to improve generalization to unseen data. This field is crucial for applications where acquiring large labeled datasets is expensive or impractical, including object detection, autonomous robotics, and anomaly detection in diverse domains. The development of robust few-shot learning methods promises significant advancements in various fields by enabling efficient model training with scarce resources.