Adversarial Information Bottleneck
文献类型：期刊论文
| 作者 | Zhai, Penglong1,2 ; Zhang, Shihua1,2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2022-05-20 |
| 页码 | 10 |
| 关键词 | Robustness Optimization Mutual information Deep learning Perturbation methods Training Task analysis Adversarial robustness deep learning hyperparameter selection information bottleneck (IB) |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2022.3172986 |
| 英文摘要 | The information bottleneck (IB) principle has been adopted to explain deep learning in terms of information compression and prediction, which are balanced by a tradeoff hyperparameter. How to optimize the IB principle for better robustness and figure out the effects of compression through the tradeoff hyperparameter are two challenging problems. Previous methods attempted to optimize the IB principle by introducing random noise into learning the representation and achieved the state-of-the-art performance in the nuisance information compression and semantic information extraction. However, their performance on resisting adversarial perturbations is far less impressive. To this end, we propose an adversarial IB (AIB) method without any explicit assumptions about the underlying distribution of the representations, which can be optimized effectively by solving a min-max optimization problem. Numerical experiments on synthetic and real-world datasets demonstrate its effectiveness on learning more invariant representations and mitigating adversarial perturbations compared to several competing IB methods. In addition, we analyze the adversarial robustness of diverse IB methods contrasting with their IB curves and reveal that IB models with the hyperparameter beta corresponding to the knee point in the IB curve achieve the best tradeoff between compression and prediction and has the best robustness against various attacks. |
| 资助项目 | National Key Research and Development Program of China[2019YFA0709501] ; National Natural Science Foundation of China[12126605] ; National Natural Science Foundation of China[61621003] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000800769800001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/61454]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Zhang, Shihua |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhai, Penglong,Zhang, Shihua. Adversarial Information Bottleneck[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:10. |
| APA | Zhai, Penglong,&Zhang, Shihua.(2022).Adversarial Information Bottleneck.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,10. |
| MLA | Zhai, Penglong,et al."Adversarial Information Bottleneck".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):10. |
入库方式： OAI收割
来源：数学与系统科学研究院
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