Few-shot learning is a machine learning approach where a model is trained to make accurate predictions or classifications using very limited labeled examples. Unlike traditional deep learning methods that require large datasets, few-shot learning focuses on scenarios where only a handful of samples—often as few as one to five per class—are available for training. This is particularly useful in domains where collecting or labeling data is expensive, time-consuming, or impractical. The core idea is to enable models to generalize effectively from minimal information by leveraging prior knowledge or patterns learned from related tasks.
A common technique in few-shot learning is meta-learning, where the model is trained on a variety of tasks to learn a strategy for quickly adapting to new tasks with limited data. For example, a model might be trained on thousands of image classification tasks, each with a small subset of classes and samples. Over time, it learns to extract features or adjust parameters in a way that can be fine-tuned rapidly for new classes. Another approach is transfer learning, where a model pre-trained on a large dataset (e.g., ImageNet) is fine-tuned on the small target dataset. For instance, a pre-trained vision model could be adapted to recognize rare animal species using just five images per species by retraining only the final classification layer while keeping earlier layers fixed.
Few-shot learning has practical applications in areas like medical imaging (diagnosing rare conditions with few scans), natural language processing (adapting to new languages with limited text), or robotics (learning tasks from minimal demonstrations). However, challenges remain. Models can struggle if the few examples are not representative of the broader class, or if the underlying task differs significantly from the pre-training data. Techniques like data augmentation, metric-based learning (e.g., Siamese networks), or leveraging synthetic data can help mitigate these issues. While not a replacement for large-scale training, few-shot learning provides a pragmatic solution for scenarios where data scarcity is a bottleneck.