How does few-shot learning deal with overfitting?

Few-shot learning addresses overfitting by leveraging specialized training strategies, architectural designs, and prior knowledge to generalize effectively from minimal data. Since traditional machine learning models often fail with small datasets—memorizing examples instead of learning patterns—few-shot approaches focus on building adaptability into the model itself. This is achieved through techniques like meta-learning, data augmentation, and transfer learning, which help the model extract broader patterns without relying on large training sets.

One key method is meta-learning, where the model is trained across many related tasks, each with its own small dataset. For example, Model-Agnostic Meta-Learning (MAML) exposes the model to diverse tasks during training, forcing it to learn a flexible initialization that can quickly adapt to new tasks with just a few examples. This approach prevents overfitting to any single task by emphasizing adaptability. Similarly, architectures like Prototypical Networks create embeddings where examples cluster by class, enabling classification based on similarity to prototypes derived from few examples. These designs inherently limit overfitting by focusing on relational patterns rather than memorizing data.

Another strategy involves using regularization and data augmentation tailored for small datasets. For instance, in image-based few-shot learning, techniques like rotation, cropping, or color jittering artificially expand the dataset’s diversity. In NLP, synonym replacement or back-translation can generate variations of text inputs. Additionally, techniques like dropout or weight regularization (e.g., L2 normalization) constrain model complexity. Pretrained models also play a critical role: models like BERT or ResNet, trained on massive datasets, provide a strong prior. When fine-tuned on few-shot tasks, only a subset of parameters (e.g., adapters or prompt layers) are updated, preserving general knowledge while adapting to new data. For example, a pretrained vision model could classify rare animal species with just five examples per class by reusing learned visual features, avoiding overfitting through parameter-efficient updates. These combined approaches enable few-shot models to generalize robustly despite limited data.