What is Few-Shot Learning?
Few-Shot Learning is the ability to learn new concepts from just a few examples, similar to how humans can quickly recognize new objects after seeing them only once or a few times.
Key Terms:
• N-way K-shot: N classes, K examples per class
• Support Set: Few labeled examples for learning
• Query Set: Test examples to classify
• Meta-Learning: Learning how to learn efficiently
🧠 Think of it like...
A child who can recognize a new animal after seeing just one picture in a book. They use their prior knowledge about animals to quickly understand this new concept!
Few-Shot Learning Methods
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Metric Learning
Learn a similarity metric to compare examples
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Model-Agnostic (MAML)
Learn initialization that adapts quickly to new tasks
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🎯
Prototypical Networks
Create class prototypes and classify by proximity
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🧠
Memory Networks
Store and retrieve relevant past experiences