Adversarial Continual Learning
This work addresses the challenge of learning new tasks without forgetting old ones in AI systems, with incremental improvements over prior approaches.
The paper tackles the problem of catastrophic forgetting in continual learning by proposing a hybrid framework that separates task-invariant and task-specific features, demonstrating effectiveness in avoiding forgetting and outperforming existing methods on image classification tasks.
Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demonstrate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at \url{https://github.com/facebookresearch/Adversarial-Continual-Learning}.