HyperMask: Adaptive Hypernetwork-based Masks for Continual Learning

TL;DR

HyperMask introduces a hypernetwork-based method that dynamically generates task-specific sparse subnetworks for continual learning, effectively mitigating catastrophic forgetting and achieving state-of-the-art results.

Contribution

The paper proposes HyperMask, a novel approach using semi-binary masks and the lottery ticket hypothesis to create adaptive, task-specific subnetworks within a single network for continual learning.

Findings

HyperMask achieves competitive results on multiple CL datasets.

It surpasses state-of-the-art scores in some scenarios.

The method effectively mitigates catastrophic forgetting.

Abstract

Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. Many continual learning (CL) strategies are trying to overcome this problem. One of the most effective is the hypernetwork-based approach. The hypernetwork generates the weights of a target model based on the task's identity. The model's main limitation is that, in practice, the hypernetwork can produce completely different architectures for subsequent tasks. To solve such a problem, we use the lottery ticket hypothesis, which postulates the existence of sparse subnetworks, named winning tickets, that preserve the performance of a whole network. In the paper, we propose a method called HyperMask, which dynamically filters a target network depending on the CL task. The hypernetwork produces semi-binary masks to obtain dedicated target subnetworks. Moreover, due to the lottery ticket hypothesis, we can use a single network with weighted subnets. Depending on the task, the importance of some weights may be dynamically enhanced while others may be weakened. HyperMask achieves competitive results in several CL datasets and, in some scenarios, goes beyond the state-of-the-art scores, both with derived and unknown task identities.