# Learning to Forget for Meta-Learning

## TL;DR

This paper introduces L2F, a method that dynamically attenuates shared initializations in meta-learning to reduce task conflicts, enabling faster adaptation and improved performance in few-shot learning scenarios.

## Contribution

The paper proposes a novel task-and-layer-wise attenuation technique called L2F that enhances MAML by reducing conflicting influences of prior knowledge, improving adaptation speed and accuracy.

## Key findings

- L2F accelerates task adaptation in few-shot learning.
- L2F improves performance of existing MAML-based frameworks.
- L2F is simple to implement and broadly applicable.

## Abstract

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.