Class-Incremental Learning via Dual Augmentation | |
Zhu Fei (朱飞); Zhen Cheng; Xu-Yao Zhang; Cheng-Lin Liu | |
2021-12-06 | |
会议日期 | Dec 6-14, 2021 |
会议地点 | Virtual |
英文摘要 | Deep learning systems typically suffer from catastrophic forgetting of past knowledge when acquiring new skills continually. In this paper, we emphasize two dilemmas, representation bias and classifier bias in class-incremental learning, and present a simple and novel approach that employs explicit class augmentation (classAug) and implicit semantic augmentation (semanAug) to address the two biases, respectively. On the one hand, we propose to address the representation bias by learning transferable and diverse representations. Specifically, we investigate the feature representations in incremental learning based on spectral analysis and present a simple technique called classAug, to let the model see more classes during training for learning representations transferable across classes. On the other hand, to overcome the classifier bias, semanAug implicitly involves the simultaneous generating of an infinite number of instances of old classes in the deep feature space, which poses tighter constraints to maintain the decision boundary of previously learned classes. Without storing any old samples, our method can perform comparably with representative data replay based approaches. |
会议录出版者 | MIT Press |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/52408] |
专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
作者单位 | 1.University of Chinese Academy of Sciences, Beijing, 100049, China 2.NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
推荐引用方式 GB/T 7714 | Zhu Fei ,Zhen Cheng,Xu-Yao Zhang,et al. Class-Incremental Learning via Dual Augmentation[C]. 见:. Virtual. Dec 6-14, 2021. |
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