Editing Few-Shot and Zero-Shot Learning (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Few-shot learning''' — Learning to classify or solve tasks with very few labeled examples per class (1–10).
* '''Zero-shot learning''' — Making predictions for classes or tasks never seen during training, using semantic descriptions.
* '''N-way K-shot''' — A standard few-shot task specification: N classes, K labeled examples per class in the support set.
* '''Zero-shot classification''' — Classifying inputs into categories not seen during training, using class descriptions or embeddings.
* '''CLIP (Contrastive Language-Image Pre-training)''' — OpenAI model that enables zero-shot image classification by comparing image embeddings to text class descriptions.
* '''In-context learning''' — LLMs performing few-shot tasks from examples in the context window, without weight updates.
* '''Semantic embeddings''' — Dense vector representations encoding semantic meaning, enabling zero-shot similarity comparisons.
* '''Class prototype''' — The average embedding of all support set examples for a class; used in Prototypical Networks for few-shot classification.
* '''Attribute-based zero-shot''' — Zero-shot learning using human-defined semantic attributes to describe each class.
* '''Generalized zero-shot learning''' — Testing on both seen and unseen classes simultaneously; harder than standard zero-shot.
* '''Imagenet zero-shot''' — CLIP achieves 75%+ accuracy on ImageNet without seeing a single ImageNet training example.
* '''Prompt-based few-shot''' — Providing 1–10 examples in the LLM prompt to demonstrate the desired task format.
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