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ErrorEraser: Unlearning Data Bias for Improved Continual Learning

11 June 2025
Xuemei Cao
Hanlin Gu
Xin Yang
Bingjun Wei
Haoyang Liang
Xiangkun Wang
Tianrui Li
    CLLKELMMU
ArXiv (abs)PDFHTML
Main:8 Pages
8 Figures
Bibliography:3 Pages
10 Tables
Appendix:1 Pages
Abstract

Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentionalthis http URLarises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction results, we find that data biases simultaneously reduce CL's ability to retain and transfer knowledge. To address this, we propose ErrorEraser, a universal plugin that removes erroneous memories caused by biases in CL, enhancing performance in both new and old tasks. ErrorEraser consists of two modules: Error Identification and Error Erasure. The former learns the probability density distribution of task data in the feature space without prior knowledge, enabling accurate identification of potentially biased samples. The latter ensures only erroneous knowledge is erased by shifting the decision space of representative outlier samples. Additionally, an incremental feature distribution learning strategy is designed to reduce the resource overhead during error identification in downstream tasks. Extensive experimental results show that ErrorEraser significantly mitigates the negative impact of data biases, achieving higher accuracy and lower forgetting rates across three types of CL methods. The code is available atthis https URL.

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@article{cao2025_2506.09347,
  title={ ErrorEraser: Unlearning Data Bias for Improved Continual Learning },
  author={ Xuemei Cao and Hanlin Gu and Xin Yang and Bingjun Wei and Haoyang Liang and Xiangkun Wang and Tianrui Li },
  journal={arXiv preprint arXiv:2506.09347},
  year={ 2025 }
}
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