few-shot-learning

Few-shot learning aims to enable models to generalize from a small number of training examples, addressing the challenge of data scarcity. It is particularly useful in scenarios where obtaining labeled data is expensive or time-consuming, such as medical imaging. A misconception is that few-shot learning provides the same performance as traditional learning with ample data; rather, it often requires more sophisticated architectures and training regimes to achieve reasonable accuracy.