What is Few-Shot Learning
Few-Shot Learning - Learning from very few labeled examples per class
A machine learning paradigm where models learn to recognize new categories from only a handful (1-10) of labeled examples. Few-shot learning enables rapid adaptation of multimodal AI systems to new domains without large-scale data collection.
How It Works
Few-shot learning models use prior knowledge from pretraining to generalize from minimal examples. Approaches include metric learning (comparing new examples to support examples in embedding space), optimization-based methods (MAML, learning to adapt quickly), and prompt-based methods (providing examples in the context window of large models). The model leverages patterns learned during pretraining to classify new items based on their similarity to the few provided examples.
Technical Details
N-way K-shot classification uses K examples each from N new classes. Prototypical networks compute class prototypes as the mean embedding of support examples and classify queries by nearest prototype. In-context learning with LLMs provides examples as part of the prompt. Performance degrades gracefully from many-shot to few-shot to zero-shot. Evaluation uses episodic testing with randomly sampled support and query sets.
Best Practices
- Use pretrained embedding models that produce well-structured representations for few-shot matching
- Select diverse and representative examples rather than random ones for the support set
- Combine few-shot learning with data augmentation to synthetically expand the training set
- Evaluate few-shot performance across multiple random episodes for reliable estimates
Common Pitfalls
- Expecting few-shot performance to match fully supervised models without domain-specific pretraining
- Not accounting for the sensitivity of few-shot learning to the choice of support examples
- Using few-shot learning when sufficient labeled data is available for standard fine-tuning
- Ignoring the computational cost of in-context learning with large language models at scale
Advanced Tips
- Use multimodal few-shot learning where examples include image-text pairs for richer context
- Implement few-shot classification for dynamic taxonomy expansion in multimodal search systems
- Apply meta-learning to pretrain models specifically for fast few-shot adaptation
- Combine few-shot learning with retrieval augmentation to find similar examples from a large database