Few-shot learning is a machine learning approach where a model learns to perform a task using only a small number of labeled examples. Unlike traditional methods that require large datasets to achieve good performance, few-shot learning focuses on leveraging prior knowledge to adapt quickly to new tasks with minimal data. This is particularly useful in scenarios where collecting or labeling data is expensive, time-consuming, or impractical. The core idea is to train a model to recognize patterns or generalize from limited information by building on pre-existing knowledge, often through techniques like transfer learning or specialized model architectures designed for data efficiency.
A common example of few-shot learning is image classification. Suppose a model has been trained on a broad dataset like ImageNet, which contains thousands of object categories. Using few-shot techniques, this model can later learn to distinguish between new classes—say, different bird species—with only a handful of labeled images per species. Another example is in natural language processing, where a language model pretrained on vast text corpora can adapt to tasks like translating rare language pairs or answering domain-specific questions with just a few examples. These scenarios highlight how few-shot learning shifts the focus from massive datasets to smart reuse of prior learning, enabling flexibility in real-world applications.
Developers implementing few-shot learning often use techniques like metric learning, where models learn to measure similarity between inputs, or optimization-based methods like Model-Agnostic Meta-Learning (MAML), which fine-tunes models to adapt rapidly to new tasks. Challenges include designing architectures that balance generalization without overfitting to the small dataset and ensuring that the pretrained knowledge aligns with the target task. For instance, a model pretrained on medical images might struggle with few-shot learning for satellite imagery if the feature representations are too dissimilar. Despite these hurdles, few-shot learning is valuable in domains like healthcare, robotics, or customer support, where data scarcity is common. By focusing on efficient knowledge transfer, it reduces dependency on large labeled datasets while maintaining practical accuracy.