concept

Few-Shot Learning

Few-shot learning is a machine learning paradigm where models are trained to learn new tasks or recognize new classes from very few labeled examples, often just one to five samples per class. It contrasts with traditional supervised learning that requires large datasets, leveraging techniques like meta-learning, metric learning, or data augmentation to generalize from limited data. This approach is particularly useful in scenarios where data collection is expensive, time-consuming, or impractical.

Also known as: Few Shot Learning, Few-Shot, FSL, Low-Shot Learning, N-Shot Learning
🧊Why learn Few-Shot Learning?

Developers should learn few-shot learning when building AI systems for domains with scarce labeled data, such as medical imaging, rare event detection, or personalized recommendations. It enables rapid adaptation to new tasks without extensive retraining, making it valuable for applications like few-shot image classification, natural language understanding with limited examples, or robotics where gathering large datasets is challenging. This skill is essential for advancing in fields like computer vision, NLP, and reinforcement learning where data efficiency is critical.

Compare Few-Shot Learning

Few-Shot Learning vs Supervised Learning→Few-Shot Learning vs Zero Shot Learning→Few-Shot Learning vs Self Supervised Learning→

Learning Resources

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Few-Shot Learning: A Survey
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PyTorch Few-Shot Learning Tutorial
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Coursera: Few-Shot Learning with MAML
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YouTube: Introduction to Few-Shot Learning
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Book: 'Meta-Learning: Theory, Algorithms, and Applications'
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Related Tools

Meta LearningTransfer LearningSemi Supervised LearningData AugmentationMetric Learning

Alternatives to Few-Shot Learning

Supervised LearningZero Shot LearningSelf Supervised Learning