Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors
This work addresses the challenge of preserving earlier knowledge in continual learning for intelligent systems, representing an incremental improvement over existing methods.
The paper tackles the problem of catastrophic forgetting in continual learning, particularly the weaker preservation of earlier learned knowledge, by proposing a fusion mechanism that includes all previous feature extractors and applies pruning to control model size. Experiments show the approach effectively reduces forgetting and achieves state-of-the-art performance on multiple classification tasks.
Continual learning of new knowledge over time is one desirable capability for intelligent systems to recognize more and more classes of objects. Without or with very limited amount of old data stored, an intelligent system often catastrophically forgets previously learned old knowledge when learning new knowledge. Recently, various approaches have been proposed to alleviate the catastrophic forgetting issue. However, old knowledge learned earlier is commonly less preserved than that learned more recently. In order to reduce the forgetting of particularly earlier learned old knowledge and improve the overall continual learning performance, we propose a simple yet effective fusion mechanism by including all the previously learned feature extractors into the intelligent model. In addition, a new feature extractor is included to the model when learning a new set of classes each time, and a feature extractor pruning is also applied to prevent the whole model size from growing rapidly. Experiments on multiple classification tasks show that the proposed approach can effectively reduce the forgetting of old knowledge, achieving state-of-the-art continual learning performance.