Few-shot learning addresses the challenge of overfitting by using prior knowledge and focusing on generalization rather than memorization. In traditional machine learning, models learn from a large amount of data, which can lead to overfitting, where the model performs well on training data but poorly on unseen data. Few-shot learning, however, operates with very limited labeled examples. To counteract overfitting, it leverages techniques such as meta-learning, where a model is trained to adapt to new tasks quickly based on previously learned tasks. This approach encourages the model to learn more about the underlying patterns in the data rather than simply memorizing specific instances.
One common technique in few-shot learning is the use of similarity measures. For instance, in image classification tasks, a model can be trained to compare images based on their features rather than classify them outright. Suppose a model has been trained on animal images and then receives only a few images of a new type of animal. Instead of attempting to learn detailed patterns from these few samples, the model can use its broader understanding of animal features to compare the new images with known categories. This reduces the likelihood of overfitting to the small dataset because the model relies on its existing knowledge to make predictions.
Another way few-shot learning mitigates overfitting is through data augmentation. Although only a few labeled examples are available, developers can generate additional synthetic training data by transforming the existing samples. For instance, flipping, rotating, or slightly altering the images can create variations that help the model generalize better. By exposing the model to a wider range of examples, even if they are derived from a few originals, it becomes less likely to focus on individual quirks of the limited data set and more on the general characteristics that define the class. Overall, few-shot learning strategies focus on building models capable of leveraging limited data without losing the ability to generalize effectively.