Few-shot learning refers to a machine learning approach where models are trained on a limited number of examples for each class, typically just a few instances. The main goal is to enable models to generalize well from these sparse data points to make accurate predictions on unseen data. There are several common approaches to few-shot learning, primarily including metric learning, model-based methods, and meta-learning.
Metric learning focuses on learning a similarity function that can differentiate between classes based on a small number of examples. In this approach, the model is trained to embed input data into a high-dimensional space where similar items are closer together. One popular example is the Siamese Network, which consists of two identical subnetworks processing inputs to determine their similarity. By using techniques like contrastive loss, the model learns to discern between similar and dissimilar pairs, allowing it to make predictions about new classes based on limited data.
Model-based methods, on the other hand, involve creating specific architectures that can adapt quickly to new classes. One example of this is the Prototypical Networks approach, where a prototype is formed for each class by averaging the feature representations of the training examples. During testing, new examples are compared to these prototypes, and predictions are made based on which prototype is nearest in the feature space. Additionally, there are hybrid methods that combine these approaches, leveraging the strengths of both metric and model-based approaches, to enhance performance on tasks with very few training samples.