Few-Shot Learning
Few-shot learning is a machine learning paradigm where a model is trained to recognize new classes from a very small number of examples, often just one or a few per class. Instead of needing extensive datasets for every new task, models learn to generalize effectively from limited data by leveraging prior knowledge or learning how to learn. This is typically achieved through techniques like meta-learning or metric-learning based approaches, where the model learns to compare new examples to existing ones.
It's like teaching a child to recognize a new animal after showing them only one picture, rather than hundreds.
Few-shot learning is vital for applications in data-scarce domains, rapid prototyping, and enabling AI to adapt quickly to new tasks without massive data collection.
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