RTRA: Rapid Training of Regularization-based Approaches in Continual Learning

• URL: http://arxiv.org/abs/2312.09361v1
• Date: Thu, 14 Dec 2023 21:51:06 GMT
• Title: RTRA: Rapid Training of Regularization-based Approaches in Continual Learning
• Authors: Sahil Nokhwal and Nirman Kumar
• Abstract summary: In regularization-based approaches to Catastrophic forgetting(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 scheme, using the Natural Gradient for loss function optimization. Our approach improves the training of regularization-based methods without sacrificing test-data performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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