Few-shot learning is a challenging problem where the goal is to achieve
generalization from only few examples. Model-agnostic meta-learning (MAML)
tackles the problem by formulating prior knowledge as a common initialization
across tasks, which is then used to quickly adapt to unseen tasks. However,
forcibly sharing an initialization can lead to conflicts among tasks and the
compromised (undesired by tasks) location on optimization landscape, thereby
hindering the task adaptation. Further, we observe that the degree of conflict
differs among not only tasks but also layers of a neural network. Thus, we
propose task-and-layer-wise attenuation on the compromised initialization to
reduce its influence. As the attenuation dynamically controls (or selectively
forgets) the influence of prior knowledge for a given task and each layer, we
name our method as L2F (Learn to Forget). The experimental results demonstrate
that the proposed method provides faster adaptation and greatly improves the
performance. Furthermore, L2F can be easily applied and improve other
state-of-the-art MAML-based frameworks, illustrating its simplicity and
generalizability.

L2F 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.