Fine-Grained Neuron Importance Guided Architecture-Based Continual Learning

Continual learning seeks to balance the stability-plasticity trade-off and address the catastrophic forgetting problem in deep neural networks. Architecture-based approaches tackle forgetting by assigning each task an isolated network subspace, which is either frozen for maximal stability or allowed small updates based on less fine-grained task information in recent gradient adjustment approaches. Notably, fine-grained task information has been shown to play a crucial role among other approaches, meaningfully contributing to guiding the training of new tasks. Inspired by this, we introduce fine-grained, neuron-level task information in this paper to gradient adjustment approaches to provide more accurate and effective training guidance. Building on the Hard Attention to the Task (HAT) architecture, we propose Fine-Grained Adaptive HAT (FG-AdaHAT), a general adaptive gradient adjustment framework for architecture-based continual learning, to which any neuron-wise importance measure and its scheduler can be applied. We propose several representative types of fine-grained importance measures for our framework constructed from training information and layer attribution methods. Experimental results show that FG-AdaHAT, using these importance measures, outperforms existing approaches on several benchmarks with long task sequences. Our code is available ( http://pengxiang-wang.com/projects/continual-learning-arena ).