Current status, application, and challenges of the interpretability of generative adversarial network models-Reference-Cited by-同舟云学术

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Current status, application, and challenges of the interpretability of generative adversarial network models

Published:2022-12-12 Issue:2 Volume:39 Page:283-314
ISSN:0824-7935
Container-title:Computational Intelligence
language:en
Short-container-title:Computational Intelligence

Author:

Wang Sulin¹,Zhao Chengqiang²^ORCID,Huang Lingling³,Li Yuanwei¹,Li Ruochen⁴

Affiliation:

1. College of Computer and Cyber Security (Oxford Brookes University) Chengdu University of Technology Chengdu China

2. School of Information Science and Technology Southwest Jiaotong University Chengdu China

3. Foreign Languages Department Sichuan University of Media and Communications Chengdu China

4. School of Economics and Management Southwest Jiaotong University Chengdu China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12564

Reference54 articles.

1. ZhuJY ParkT IsolaP EfrosAA.Unpaired image‐to‐image translation using cycle‐consistent adversarial networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV); 2017. doi:10.1109/iccv.2017.244

2. Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis

3. Vessel-GAN: Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks

4. BP-GAN: Interpretable Human Branchpoint Prediction Using Attentive Generative Adversarial Networks

5. LiH LinY MuellerK XuW.Interpreting galaxy deblender GAN from the discriminator's perspective. Advances in Visual Computing;2020:239‐250.10.1007/978‐3‐030‐64559‐5_18

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