Few-shot learning - Wikipedia
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2026-07-22 10:02:21
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Text Highlight2026-07-22 16:26:21
Original Highlight Excerpt
"The limiting case of few-shot learning is zero-shot learning that requires no training on examples of the class to be classified"
Whisper Note
So zero-shot is just few-shot with zero examples, neat way to frame it.
Text Highlight2026-07-22 13:23:21
Original Highlight Excerpt
"One-shot learning is the special case of the N-way K-shot framing in which K equals one"
Whisper Note
Wait, so zero-shot would be K=0? That seems kind of obvious once you say it.
Text Highlight2026-07-22 13:14:21
Original Highlight Excerpt
"One-shot learning is the special case of the N-way K-shot framing in which K equals one"
Whisper Note
So basically one-shot is just a fancy name for K=1, got it.
Text Highlight2026-07-22 10:20:21
Original Highlight Excerpt
"Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small…"
Whisper Note
I've tried few-shot learning before and it's way harder than it sounds, but the idea is solid.
Text Highlight2026-07-22 10:11:21
Original Highlight Excerpt
"Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small…"
Whisper Note
This is what we need in medical imaging where labeled data is scarce and expensive to get.
Text Highlight2026-07-22 10:02:21
Original Highlight Excerpt
"Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small…"
Whisper Note
So it's basically teaching a model with like 5 examples instead of 5,000? Sounds too good to be true.
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