What does few-shot learning mean?

It leverages prior knowledge gained from training on large, diverse datasets. This approach relies heavily on the model's ability to transfer knowledge and find similarities between new examples and patterns seen before. It typically utilizes support sets (few examples per class) and query sets for evaluation. Performance is highly dependent on the relevance of the pre-training data and the underlying similarity of the new task.

It is primarily applied where acquiring large labeled datasets is impractical or expensive. Key use cases include adapting pre-trained language models for niche NLP tasks, specialized image recognition, and low-resource settings. This approach provides value by increasing model adaptability and reducing data labeling costs.