NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks
This addresses the challenge of continual learning for AI systems that need to adapt to new tasks over time without forgetting, though it is incremental as it builds on existing sparse network methods.
The paper tackles the problem of continual learning by proposing NISPA, a sparse neural network architecture that maintains performance on older tasks and learns new ones with minimal overhead, achieving significant performance improvements and using up to ten times fewer parameters than state-of-the-art baselines on datasets like EMNIST and CIFAR.
The goal of continual learning (CL) is to learn different tasks over time. The main desiderata associated with CL are to maintain performance on older tasks, leverage the latter to improve learning of future tasks, and to introduce minimal overhead in the training process (for instance, to not require a growing model or retraining). We propose the Neuro-Inspired Stability-Plasticity Adaptation (NISPA) architecture that addresses these desiderata through a sparse neural network with fixed density. NISPA forms stable paths to preserve learned knowledge from older tasks. Also, NISPA uses connection rewiring to create new plastic paths that reuse existing knowledge on novel tasks. Our extensive evaluation on EMNIST, FashionMNIST, CIFAR10, and CIFAR100 datasets shows that NISPA significantly outperforms representative state-of-the-art continual learning baselines, and it uses up to ten times fewer learnable parameters compared to baselines. We also make the case that sparsity is an essential ingredient for continual learning. The NISPA code is available at https://github.com/BurakGurbuz97/NISPA.