Borji, A.[Ali],
Pros and cons of GAN evaluation measures,
CVIU(179), 2019, pp. 41-65.
Elsevier DOI
1903
Generative adversarial nets, Generative models, Evaluation,
Deep learning, Neural networks
BibRef
Peng, Y.[Ye],
Zhao, W.T.[Wen-Tao],
Cai, W.[Wei],
Su, J.S.[Jin-Shu],
Han, B.[Biao],
Liu, Q.A.[Qi-Ang],
Evaluating Deep Learning for Image Classification in Adversarial
Environment,
IEICE(E103-D), No. 4, April 2020, pp. 825-837.
WWW Link.
2004
BibRef
Miller, D.J.,
Xiang, Z.,
Kesidis, G.,
Adversarial Learning Targeting Deep Neural Network Classification:
A Comprehensive Review of Defenses Against Attacks,
PIEEE(108), No. 3, March 2020, pp. 402-433.
IEEE DOI
2003
Training data, Neural networks, Reverse engineering,
Machine learning, Robustness, Training data, Feature extraction, white box
BibRef
Bai, X.[Xiao],
Wang, X.[Xiang],
Liu, X.L.[Xiang-Long],
Liu, Q.[Qiang],
Song, J.K.[Jing-Kuan],
Sebe, N.[Nicu],
Kim, B.[Been],
Explainable deep learning for efficient and robust pattern
recognition: A survey of recent developments,
PR(120), 2021, pp. 108102.
Elsevier DOI
2109
Survey, Explainable Learning. Explainable deep learning,
Network compression and acceleration, Adversarial robustness,
Stability in deep learning
BibRef
Zhang, X.W.[Xing-Wei],
Zheng, X.L.[Xiao-Long],
Mao, W.J.[Wen-Ji],
Adversarial Perturbation Defense on Deep Neural Networks,
Surveys(54), No. 8, October 2021, pp. xx-yy.
DOI Link
2110
Survey, Adversarial Defense. security, deep neural networks, origin, Adversarial perturbation defense
BibRef
Liu, M.Y.[Ming-Yu],
Huang, X.[Xun],
Yu, J.H.[Jia-Hui],
Wang, T.C.[Ting-Chun],
Mallya, A.[Arun],
Generative Adversarial Networks for Image and Video Synthesis:
Algorithms and Applications,
PIEEE(109), No. 5, May 2021, pp. 839-862.
IEEE DOI
2105
Survey, Image Synthesis. Generators, Training,
Generative adversarial networks, Linear programming,
neural rendering
BibRef
Qian, Z.[Zhuang],
Huang, K.[Kaizhu],
Wang, Q.F.[Qiu-Feng],
Zhang, X.Y.[Xu-Yao],
A survey of robust adversarial training in pattern recognition:
Fundamental, theory, and methodologies,
PR(131), 2022, pp. 108889.
Elsevier DOI
2208
Survey, GAN Training. Adversarial examples, Adversarial training, Robust learning
BibRef
Zhou, S.[Shuai],
Liu, C.[Chi],
Ye, D.[Dayong],
Zhu, T.Q.[Tian-Qing],
Zhou, W.[Wanlei],
Yu, P.S.[Philip S.],
Adversarial Attacks and Defenses in Deep Learning:
From a Perspective of Cybersecurity,
Surveys(55), No. 8, December 2022, pp. xx-yy.
DOI Link
2301
cybersecurity, adversarial attacks and defenses,
advanced persistent threats, Deep learning
BibRef
Buzuti, L.F.[Lucas F.],
Thomaz, C.E.[Carlos E.],
Fréchet AutoEncoder Distance: A new approach for evaluation of
Generative Adversarial Networks,
CVIU(235), 2023, pp. 103768.
Elsevier DOI
2310
Generative adversarial networks, Autoencoder,
Feature extraction, Fréchet distance, Evaluation, Measure
BibRef
Aldausari, N.[Nuha],
Sowmya, A.[Arcot],
Marcus, N.[Nadine],
Mohammadi, G.[Gelareh],
Video Generative Adversarial Networks: A Review,
Surveys(55), No. 2, February 2023, pp. xx-yy.
DOI Link
2212
video synthesis, conditional generation,
Generative Adversarial Networks, multimodal data
BibRef
Machado, G.R.[Gabriel Resende],
Silva, E.[Eugenio],
Goldschmidt, R.R.[Ronaldo Ribeiro],
Adversarial Machine Learning in Image Classification:
A Survey Toward the Defender's Perspective,
Surveys(55), No. 1, January 2023, pp. xx-yy.
DOI Link
2212
adversarial attacks, deep neural networks, adversarial images,
defense methods, image classification
BibRef
Zhang, C.H.[Chen-Han],
Yu, S.[Shui],
Tian, Z.Y.[Zhi-Yi],
Yu, J.J.Q.[James J. Q.],
Generative Adversarial Networks: A Survey on Attack and Defense
Perspective,
Surveys(56), No. 4, November 2023, pp. xx-yy.
DOI Link
2312
Survey, GAN Attacks. security and privacy, GANs survey, deep learning,
attack and defense, Generative adversarial networks
BibRef
Xia, W.H.[Wei-Hao],
Xue, J.H.[Jing-Hao],
A Survey on Deep Generative 3D-Aware Image Synthesis,
Surveys(56), No. 4, November 2023, pp. xx-yy.
DOI Link
2312
deep generative models, diffusion probabilistic models,
implicit neural representation, 3D-aware image synthesis,
generative adversarial network
BibRef
Guo, J.[Jun],
Bao, W.[Wei],
Wang, J.K.[Jia-Kai],
Ma, Y.Q.[Yu-Qing],
Gao, X.H.[Xing-Hai],
Xiao, G.[Gang],
Liu, A.[Aishan],
Dong, J.[Jian],
Liu, X.L.[Xiang-Long],
Wu, W.J.[Wen-Jun],
A comprehensive evaluation framework for deep model robustness,
PR(137), 2023, pp. 109308.
Elsevier DOI
2302
Adversarial examples, Evaluation metrics, Model robustness
BibRef
Yu, Y.R.[Yun-Rui],
Gao, X.T.[Xi-Tong],
Xu, C.Z.[Cheng-Zhong],
LAFIT: Efficient and Reliable Evaluation of Adversarial Defenses With
Latent Features,
PAMI(46), No. 1, January 2024, pp. 354-369.
IEEE DOI
2312
BibRef
Li, Y.J.[Yan-Jie],
Xie, B.[Bin],
Guo, S.T.[Song-Tao],
Yang, Y.Y.[Yuan-Yuan],
Xiao, B.[Bin],
A Survey of Robustness and Safety of 2D and 3D Deep Learning Models
against Adversarial Attacks,
Surveys(56), No. 6, January 2024, pp. xx-yy.
DOI Link
2404
Deep learning, 3D computer vision, adversarial attack, robustness
BibRef
Dong, J.H.[Jun-Hao],
Chen, J.X.[Jun-Xi],
Xie, X.H.[Xiao-Hua],
Lai, J.H.[Jian-Huang],
Chen, H.[Hao],
Survey on Adversarial Attack and Defense for Medical Image Analysis:
Methods and Challenges,
Surveys(57), No. 3, November 2024, pp. xx-yy.
DOI Link
2501
Survey, Adversarial Attack. Adversarial machine learning, medical image analysis,
deep learning, adversarial example, evaluation
BibRef
Mahima, K.T.Y.[K. T. Yasas],
Perera, A.G.[Asanka G.],
Anavatti, S.[Sreenatha],
Garratt, M.[Matt],
Toward Robust 3D Perception for Autonomous Vehicles:
A Review of Adversarial Attacks and Countermeasures,
ITS(25), No. 12, December 2024, pp. 19176-19202.
IEEE DOI
2412
Solid modeling, Sensors, Adversarial machine learning,
Autonomous vehicles, Security, Reviews, Adversarial attacks, LiDAR
BibRef
Wei, H.[Hui],
Tang, H.[Hao],
Jia, X.M.[Xue-Mei],
Wang, Z.X.[Zhi-Xiang],
Yu, H.[Hanxun],
Li, Z.[Zhubo],
Satoh, S.[Shin'ichi],
Van Gool, L.J.[Luc J.],
Wang, Z.[Zheng],
Physical Adversarial Attack Meets Computer Vision: A Decade Survey,
PAMI(46), No. 12, December 2024, pp. 9797-9817.
IEEE DOI
2411
Survey, Adversarial Attacks. Perturbation methods, Data models, Biological system modeling,
Task analysis, Predictive models, Surveys, Adversarial attack,
survey
BibRef
Deshpande, R.[Rucha],
Anastasio, M.A.[Mark A.],
Brooks, F.J.[Frank J.],
A method for evaluating deep generative models of images for
hallucinations in high-order spatial context,
PRL(186), 2024, pp. 23-29.
Elsevier DOI
2412
Deep generative model evaluation, Hallucination,
Stochastic context models, Benchmark, Dataset,
Generative adversarial networks
BibRef
Yang, M.P.[Meng-Ping],
Wang, Z.[Zhe],
Image Synthesis Under Limited Data: A Survey and Taxonomy,
IJCV(133), No. 6, June 2025, pp. Psges 3689-3726.
Springer DOI Code:
WWW Link.
2505
BibRef
Zhao, G.N.[Gan-Ning],
Magoulianitis, V.[Vasileios],
You, S.[Suya],
Kuo, C.C.J.[C.C. Jay],
A Lightweight Generalizable Evaluation and Enhancement Framework for
Generative Models and Generated Samples,
VAQuality24(450-459)
IEEE DOI
2404
Deep learning, Performance evaluation, Learning systems,
Correlation, Filtering, Conferences
BibRef
Luzi, L.[Lorenzo],
Marrero, C.O.[Carlos Ortiz],
Wynar, N.[Nile],
Baraniuk, R.G.[Richard G.],
Henry, M.J.[Michael J.],
Evaluating generative networks using Gaussian mixtures of image
features,
WACV23(279-288)
IEEE DOI
2302
Image resolution, Inverse problems, Computational modeling,
Perturbation methods, Gaussian noise, Gaussian distribution,
adversarial attack and defense methods
BibRef
Lee, J.[Junghyuk],
Lee, J.S.[Jong-Seok],
TREND: Truncated Generalized Normal Density Estimation of Inception
Embeddings for GAN Evaluation,
ECCV22(XXIII:87-103).
Springer DOI
2211
BibRef
Parmar, G.[Gaurav],
Zhang, R.[Richard],
Zhu, J.Y.[Jun-Yan],
On Aliased Resizing and Surprising Subtleties in GAN Evaluation,
CVPR22(11400-11410)
IEEE DOI
2210
Measurement, Training, Image coding, Computational modeling,
Pipelines, Transform coding, Signal processing, Vision + graphics
BibRef
Liu, Y.[Ye],
Cheng, Y.[Yaya],
Gao, L.L.[Lian-Li],
Liu, X.L.[Xiang-Long],
Zhang, Q.L.[Qi-Long],
Song, J.K.[Jing-Kuan],
Practical Evaluation of Adversarial Robustness via Adaptive Auto
Attack,
CVPR22(15084-15093)
IEEE DOI
2210
Adaptation models, Codes, Computational modeling, Robustness,
Iterative methods, Adversarial attack and defense
BibRef
ircelj, J.[Jaka],
Skocaj, D.[Danijel],
Accuracy-Perturbation Curves for Evaluation of Adversarial Attack and
Defence Methods,
ICPR21(6290-6297)
IEEE DOI
2105
Training, Visualization, Perturbation methods, Machine learning,
Robustness, Generators
BibRef
Wang, J.Y.[Jin-Yu],
Li, Y.[Yang],
Yang, H.T.[Hai-Tao],
Zheng, F.J.[Feng-Jie],
Gao, Y.G.[Yu-Ge],
Li, G.Y.[Gao-Yuan],
GAN Evaluation Method Based on Remote Sensing Image Information,
ICIVC21(295-300)
IEEE DOI
2112
Training, Visualization, Uncertainty, Stability criteria,
Optimization methods, Generative adversarial networks, GAN
BibRef
Torfi, A.[Amirsina],
Beyki, M.[Mohammadreza],
Fox, E.A.[Edward A.],
On the Evaluation of Generative Adversarial Networks By
Discriminative Models,
ICPR21(991-998)
IEEE DOI
2105
Measurement, Training, Visualization, Technological innovation,
Neural networks, Estimation, Generative adversarial networks
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
VAE, Variational Autoencoder .