Supervised learning and few-shot learning are both machine learning approaches but differ fundamentally in how they use training data and handle generalization. Supervised learning relies on large, labeled datasets to train models by example. Each input (e.g., an image, text snippet, or sensor reading) is paired with a corresponding output label (e.g., “cat,” “spam email,” or “defective part”). The model learns to map inputs to outputs by iteratively adjusting its parameters to minimize prediction errors. For example, a supervised image classifier might require thousands of labeled images of cats and dogs to reliably distinguish between them. The key assumption is that the training data comprehensively represents the problem space, and the model’s performance depends heavily on the quantity and quality of labeled examples.
Few-shot learning, by contrast, is designed to learn new concepts or tasks with minimal labeled data—often as few as one to five examples. This approach is useful when acquiring large labeled datasets is impractical, such as in medical imaging (rare diseases) or custom product categorization. Instead of training from scratch on every new task, few-shot models leverage prior knowledge gained during a meta-training phase, where they learn to generalize across diverse tasks. For instance, a few-shot language model trained on many text tasks (translation, summarization) could adapt to a new task like detecting sarcasm with just a handful of labeled examples. Techniques like metric learning (comparing new examples to known ones) or parameter-efficient fine-tuning (updating only parts of the model) are common in few-shot setups.
The main differences lie in data requirements and adaptability. Supervised learning demands ample labeled data for each specific task and struggles with unseen classes without retraining. Few-shot learning emphasizes flexibility: models are pre-trained to extract reusable patterns and adapt quickly to new tasks with minimal data. For example, a supervised model trained to classify 100 animal species cannot recognize a new species without additional labeled data, while a few-shot model could infer the new class using a small reference set. Developers choose supervised learning for stable, well-defined problems with abundant data (e.g., speech recognition) and few-shot learning for dynamic or niche scenarios where data is scarce (e.g., custom voice commands for specialized devices).