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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.17054 |
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| _version_ | 1866916261969002496 |
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| author | Xiao, Hanxi Lyu, Fan |
| author_facet | Xiao, Hanxi Lyu, Fan |
| contents | The goal of Continual Learning (CL) task is to continuously learn multiple new tasks sequentially while achieving a balance between the plasticity and stability of new and old knowledge. This paper analyzes that this insufficiency arises from the ineffective handling of outliers, leading to abnormal gradients and unexpected model updates. To address this issue, we enhance the data-aware and parameter-aware robustness of CL, proposing a Robust Continual Learning (RCL) method. From the data perspective, we develop a contrastive loss based on the concepts of uniformity and alignment, forming a feature distribution that is more applicable to outliers. From the parameter perspective, we present a forward strategy for worst-case perturbation and apply robust gradient projection to the parameters. The experimental results on three benchmarks show that the proposed method effectively maintains robustness and achieves new state-of-the-art (SOTA) results. The code is available at: https://github.com/HanxiXiao/RCL |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17054 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Data-aware and Parameter-aware Robustness for Continual Learning Xiao, Hanxi Lyu, Fan Machine Learning The goal of Continual Learning (CL) task is to continuously learn multiple new tasks sequentially while achieving a balance between the plasticity and stability of new and old knowledge. This paper analyzes that this insufficiency arises from the ineffective handling of outliers, leading to abnormal gradients and unexpected model updates. To address this issue, we enhance the data-aware and parameter-aware robustness of CL, proposing a Robust Continual Learning (RCL) method. From the data perspective, we develop a contrastive loss based on the concepts of uniformity and alignment, forming a feature distribution that is more applicable to outliers. From the parameter perspective, we present a forward strategy for worst-case perturbation and apply robust gradient projection to the parameters. The experimental results on three benchmarks show that the proposed method effectively maintains robustness and achieves new state-of-the-art (SOTA) results. The code is available at: https://github.com/HanxiXiao/RCL |
| title | Improving Data-aware and Parameter-aware Robustness for Continual Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.17054 |