Few-shot learning algorithms enable models to perform tasks with very limited labeled examples by leveraging prior knowledge or structural assumptions. Three widely used methods are Model-Agnostic Meta-Learning (MAML), Prototypical Networks, and Matching Networks. Each addresses the challenge of learning from small datasets through distinct approaches, balancing flexibility, efficiency, and simplicity for practical use cases.
Model-Agnostic Meta-Learning (MAML) trains models to adapt quickly to new tasks. Instead of focusing on a single task, MAML exposes the model to many related tasks during training. For each task, the model performs a few gradient updates on a small support set and evaluates on a query set. The key idea is to optimize the model’s initial parameters so that these quick updates yield strong performance. For example, in image classification, MAML could learn to recognize new animal species with just five examples by fine-tuning from a base model trained on diverse species. Developers often implement MAML with neural networks, using frameworks like PyTorch or TensorFlow to automate task sampling and nested optimization.
Prototypical Networks simplify few-shot learning by comparing new examples to class prototypes. Each class prototype is the average embedding of its support examples. During inference, the model computes distances (e.g., Euclidean or cosine) between a test example and all prototypes, assigning the class with the closest match. This approach works well when classes are separable in the embedding space. For instance, in text classification, a prototype could represent the average vector of sentences labeled “complaint” or “inquiry,” enabling the model to categorize new sentences with minimal examples. Prototypical Networks are computationally efficient and avoid complex meta-learning setups, making them accessible for projects with limited resources.
Matching Networks combine embedding and attention mechanisms to weigh support examples when predicting labels for queries. The model encodes both support and query examples into a shared space, then uses attention to compute similarity scores between a query and each support instance. These scores determine how much each support example influences the query’s predicted label. For example, in a medical diagnosis system, Matching Networks could identify rare diseases by comparing patient symptoms to a handful of documented cases. The approach is flexible and works with variable-sized support sets, but it requires careful design of the embedding and attention functions to ensure meaningful comparisons. Libraries like Hugging Face Transformers provide tools to adapt attention-based models for such tasks.