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

Sahil Nokhwal; Nirman Kumar

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arXiv · 2312.09361

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

Sahil Nokhwal·Nirman Kumar

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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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Sahil Nokhwal, Nirman Kumar. 2023-12-14. RTRA: Rapid Training of Regularization-based Approaches in Continual Learning. https://arxiv.org/abs/2312.09361

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