  Catastrophic forgetting, the phenomenon in which a neural network loses
previously obtained knowledge during the learning of new tasks, poses a
significant challenge in continual learning. The Hard-Attention-to-the-Task
(HAT) mechanism has shown potential in mitigating this problem, but its
practical implementation has been complicated by issues of usability and
compatibility, and a lack of support for existing network reuse. In this paper,
we introduce HAT-CL, a user-friendly, PyTorch-compatible redesign of the HAT
mechanism. HAT-CL not only automates gradient manipulation but also streamlines
the transformation of PyTorch modules into HAT modules. It achieves this by
providing a comprehensive suite of modules that can be seamlessly integrated
into existing architectures. Additionally, HAT-CL offers ready-to-use HAT
networks that are smoothly integrated with the TIMM library. Beyond the
redesign and reimplementation of HAT, we also introduce novel mask manipulation
techniques for HAT, which have consistently shown improvements across various
experiments. Our work paves the way for a broader application of the HAT
mechanism, opening up new possibilities in continual learning across diverse
models and applications.
