Few-Shot Learning (FSL) is a Machine Learning framework that allows models to generalize to new categories with only a few labeled examples, mimicking human learning. This approach addresses challenges like the scarcity of annotated data and the computational cost of retraining models when new data becomes available. FSL uses
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PrerequisitesWhat is Few-Shot Learning?Why Few-Shot Learning?How does Few-Shot Learning work?Approaches for Few-Shot LearningOne-Shot LearningApplications of Few-Shot LearningConclusion1 Comment
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