TL;DRL2F method proposes task-and-layer-wise attenuation to reduce the influence of prior knowledge for faster adaptation and improved performance in few-shot learning tasks with neural networks.
Abstract
few-shot learning is a challenging problem where the system is required to achieve generalization from only few examples. Meta-learning tackles the problem by learning prior knowledge shared across a distribution of tasks, which is then used to quickly adapt to unseen tasks.