Fine-Grained Gradient Restriction: A Simple Approach for Mitigating Catastrophic Forgetting
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914961831231488 |
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| author | Liu, Bo Ye, Mao Stone, Peter Liu, Qiang |
| author_facet | Liu, Bo Ye, Mao Stone, Peter Liu, Qiang |
| contents | A fundamental challenge in continual learning is to balance the trade-off between learning new tasks and remembering the previously acquired knowledge. Gradient Episodic Memory (GEM) achieves this balance by utilizing a subset of past training samples to restrict the update direction of the model parameters. In this work, we start by analyzing an often overlooked hyper-parameter in GEM, the memory strength, which boosts the empirical performance by further constraining the update direction. We show that memory strength is effective mainly because it improves GEM's generalization ability and therefore leads to a more favorable trade-off. By this finding, we propose two approaches that more flexibly constrain the update direction. Our methods are able to achieve uniformly better Pareto Frontiers of remembering old and learning new knowledge than using memory strength. We further propose a computationally efficient method to approximately solve the optimization problem with more constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fine-Grained Gradient Restriction: A Simple Approach for Mitigating Catastrophic Forgetting Liu, Bo Ye, Mao Stone, Peter Liu, Qiang Machine Learning A fundamental challenge in continual learning is to balance the trade-off between learning new tasks and remembering the previously acquired knowledge. Gradient Episodic Memory (GEM) achieves this balance by utilizing a subset of past training samples to restrict the update direction of the model parameters. In this work, we start by analyzing an often overlooked hyper-parameter in GEM, the memory strength, which boosts the empirical performance by further constraining the update direction. We show that memory strength is effective mainly because it improves GEM's generalization ability and therefore leads to a more favorable trade-off. By this finding, we propose two approaches that more flexibly constrain the update direction. Our methods are able to achieve uniformly better Pareto Frontiers of remembering old and learning new knowledge than using memory strength. We further propose a computationally efficient method to approximately solve the optimization problem with more constraints. |
| title | Fine-Grained Gradient Restriction: A Simple Approach for Mitigating Catastrophic Forgetting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.00868 |