How does few-shot learning help with multi-class classification problems?

Few-shot learning helps multi-class classification by enabling models to recognize new classes with very few labeled examples, reducing reliance on large datasets. In traditional multi-class setups, each class requires hundreds or thousands of labeled samples to train accurate models. Few-shot learning shifts this paradigm by teaching models to generalize from limited data. For example, if a model needs to classify 50 animal species but only has five images per rare species, few-shot techniques allow it to infer patterns from related classes (e.g., shared features between similar animals) to make accurate predictions. This is particularly useful when acquiring labeled data is expensive or impractical.

The core mechanism involves learning a feature space where similar classes are clustered, and differences between classes are amplified. Techniques like meta-learning train models on “tasks” that simulate few-shot scenarios. For instance, a model might repeatedly practice classifying random subsets of classes (e.g., 10 classes at a time) with minimal examples, forcing it to adapt quickly. Prototypical Networks, a popular approach, create a prototype (average feature vector) for each class using its few examples. New instances are classified by comparing their features to these prototypes. This approach works well in multi-class setups because prototypes can be computed for all classes simultaneously, and distances in the feature space directly translate to class probabilities.

Practical applications include scenarios where classes are added dynamically or data is scarce. In customer support ticket categorization, a model might need to classify tickets into 30 categories, but some categories have only three historical examples. A few-shot model can leverage embeddings from pretrained language models (e.g., BERT) to represent text, then compute similarity to existing class prototypes. Similarly, in medical imaging, classifying rare diseases with few annotated scans becomes feasible by transferring knowledge from common diseases. Developers can implement this using frameworks like PyTorch or TensorFlow: define a feature extractor, compute prototypes for each class, and use cosine similarity for classification. This approach scales efficiently, as adding a new class only requires computing its prototype from a handful of samples.