RTRA: Rapid Training of Regularization-based Approaches in Continual Learning
This work addresses the problem of catastrophic forgetting for continual learning practitioners, but it is incremental as it builds on existing regularization-based methods.
The authors tackled catastrophic forgetting in continual learning by proposing RTRA, a modification to Elastic Weight Consolidation using Natural Gradient optimization, which improved training speed without sacrificing test performance on the iFood251 dataset.
Catastrophic forgetting(CF) is a significant challenge in continual learning (CL). In regularization-based approaches to mitigate CF, modifications to important training parameters are penalized in subsequent tasks using an appropriate loss function. We propose the RTRA, a modification to the widely used Elastic Weight Consolidation (EWC) regularization scheme, using the Natural Gradient for loss function optimization. Our approach improves the training of regularization-based methods without sacrificing test-data performance. We compare the proposed RTRA approach against EWC using the iFood251 dataset. We show that RTRA has a clear edge over the state-of-the-art approaches.