Spectrum of advancements and developments in multidisciplinary domains for generative adversarial networks (GANs)

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365 · see citing works

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143 · see citing works

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,281 · see citing works

Untitled work

(2017) · International Conference on Machine Learning · Cited by 6,382 · see citing works

StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00916 · Cited by 4,163 · see citing works

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332 · see citing works

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258 · see citing works

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

Papernot, N., McDaniel, P. D., Wu, X., Jha, S., & Swami, A. (2016) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2016.41 · Cited by 3,123 · see citing works

StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks

Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.629 · Cited by 3,021 · see citing works

Conditional Image Synthesis With Auxiliary Classifier GANsPreprint

Odena, A., Olah, C., & Shlens, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09585 · Cited by 2,968 · see citing works

Generative Adversarial Text to Image Synthesis

Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494 · see citing works

Towards Principled Methods for Training Generative Adversarial NetworksPreprint

Arjovsky, M., & Bottou, L. (2017) · International Conference on Learning Representations · doi:10.48550/arxiv.1701.04862 · Cited by 2,373 · see citing works

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Akhtar, N., & Mian, A. (2018) · IEEE Access · doi:10.1109/access.2018.2807385 · Cited by 2,021 · see citing works

Learning from Simulated and Unsupervised Images through Adversarial Training

Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.241 · Cited by 1,831 · see citing works

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works

SEGAN: Speech Enhancement Generative Adversarial Network

Pascual, S., Bonafonte, A., & Serrà, J. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1428 · Cited by 1,351 · see citing works

A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231 · see citing works

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2856256 · Cited by 1,222 · see citing works

Unrolled Generative Adversarial NetworksPreprint

Metz, L., Poole, B., Pfau, D., & Sohl-Dickstein, J. N. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1611.02163 · Cited by 1,100 · see citing works

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P. L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2785879 · Cited by 1,075 · see citing works

Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition

Fukushima, K., & Miyake, S. (1982) · Lecture Notes in Biomathematics · doi:10.1007/978-3-642-46466-9_18 · Cited by 991 · see citing works

The security of machine learning

Barreno, M., Nelson, B. A., Joseph, A. D., & Tygar, J. D. (2010) · Machine Learning · doi:10.1007/s10994-010-5188-5 · Cited by 839 · see citing works

Generative adversarial networks: introduction and outlook

Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., & Wang, F. (2017) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510583 · Cited by 815 · see citing works

Adversarial learning

Lowd, D., & Meek, C. (2005) · Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining · doi:10.1145/1081870.1081950 · Cited by 755 · see citing works

MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

Dong, H.-W., Hsiao, W.-Y., Yang, L.-C., & Yang, Y.-H. (2017) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11312 · Cited by 714 · see citing works

Recent Progress on Generative Adversarial Networks (GANs): A Survey

Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2905015 · Cited by 699 · see citing works

Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.

Fredrikson, M., Lantz, E., Jha, S., Lin, S. M., Page, D., & Ristenpart, T. (2014) · PubMed · Cited by 658 · see citing works

DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems

Zhang, M., Zhang, Y., Zhang, L., Liu, C., & Khurshid, S. (2018) · Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering · doi:10.1145/3238147.3238187 · Cited by 611 · see citing works

Quantum Generative Adversarial Learning

Lloyd, S., & Weedbrook, C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.040502 · Cited by 576 · see citing works

A Survey on Metamorphic Testing

Segura, S., Fraser, G., Sánchez, A. B., & Ruiz–Cortés, A. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2532875 · Cited by 571 · see citing works

Deep learning fault diagnosis method based on global optimization GAN for unbalanced data

Zhou, F., Yang, S., Fujita, H., Chen, D., & Wen, C. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.07.008 · Cited by 570 · see citing works

Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN

Hu, W., & Tan, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-8991-9_29 · Cited by 547 · see citing works

Deep Generative Design: Integration of Topology Optimization and Generative Models

Oh, S., Jung, Y., Kim, S., Lee, I., & Kang, N. (2019) · Journal of Mechanical Design · doi:10.1115/1.4044229 · Cited by 469 · see citing works

Generalization and Equilibrium in Generative Adversarial Nets (GANs)Preprint

Arora, S., Ge, R., Liang, Y., Ma, T., & Zhang, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00573 · Cited by 468 · see citing works

Large-scale malware classification using random projections and neural networks

Dahl, G. E., Stokes, J. W., Deng, L., & Yu, D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638293 · Cited by 450 · see citing works

Quantum Generative Adversarial Networks for learning and loading random distributions

Zoufal, C., Lucchi, A., & Woerner, S. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000382432 · Cited by 390 · see citing works

FairGAN: Fairness-aware Generative Adversarial Networks

Xu, D., Yuan, S., Zhang, L., & Wu, X. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8622525 · Cited by 379 · see citing works

Generating Adversarial Malware Examples for Black-Box Attacks Based on GANPreprint

Hu, W., & Tan, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05983 · Cited by 361 · see citing works

Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities

Qi, G.-J. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01265-2 · Cited by 355 · see citing works

Adversarial Ranking for Language GenerationPreprint

Lin, K., Li, D., He, X.-D., Zhang, Z.-Y., & Sun, M.-T. (2017) · Neural Information Processing Systems · doi:10.48550/arxiv.1705.11001 · Cited by 354 · see citing works

IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection

Lin, Z., Shi, Y., & Xue, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05981-0_7 · Cited by 347 · see citing works

Optimizing the Latent Space of Generative NetworksPreprint

Bojanowski, P., Joulin, A., López-Paz, D., & Szlam, A. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.05776 · Cited by 341 · see citing works

Detecting GAN generated Fake Images using Co-occurrence Matrices

Nataraj, L., Mohammed, T. M., Manjunath, B. S., Chandrasekaran, S., Flenner, A., Bappy, J. H., & Roy-Chowdhury, A. K. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.5.mwsf-532 · Cited by 331 · see citing works

Adversarial Perturbations Against Deep Neural Networks for Malware ClassificationPreprint

Grosse, K., Papernot, N., Manoharan, P., Michael Backes, A., & McDaniel, P. D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04435 · Cited by 315 · see citing works

MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music GenerationPreprint

Yang, L.-C., Chou, S.-Y., & Yang, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10847 · Cited by 308 · see citing works

Multi-agent Diverse Generative Adversarial Networks

Ghosh, A., Kulharia, V., Namboodiri, V. P., Torr, P. H. S., & Dokania, P. K. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00888 · Cited by 306 · see citing works

Analysis of classifiers’ robustness to adversarial perturbations

Fawzi, A., Fawzi, O., & Pascal Frossard, B. (2017) · Machine Learning · doi:10.1007/s10994-017-5663-3 · Cited by 289 · see citing works

Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

Wang, Z., She, Q., & Ward, T. E. (2022) · ACM Computing Surveys · doi:10.1145/3439723 · Cited by 267 · see citing works

Towards Bayesian Deep Learning: A Framework and Some Existing Methods

Wang, H., & Yeung, D. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2606428 · Cited by 254 · see citing works

Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition

Donahue, C., Li, B., & Prabhavalkar, R. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462581 · Cited by 237 · see citing works

An empirical study on evaluation metrics of generative adversarial networksPreprint

Xu, Q., Huang, G. M., Yuan, Y., Guo, C., Sun, Y., Wu, F., & Weinberger, K. Q. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07755 · Cited by 223 · see citing works

Architectural Drawings Recognition and Generation through Machine Learning

Huang, W., & Zheng, H. R. (2018) · ACADIA quarterly · doi:10.52842/conf.acadia.2018.156 · Cited by 208 · see citing works

DeepDGA

Anderson, H. S., Woodbridge, J., & Filar, B. (2016) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2996758.2996767 · Cited by 202 · see citing works

A survey of image synthesis and editing with generative adversarial networks

Wu, X., Xu, K., & Hall, P. M. (2017) · Tsinghua Science & Technology · doi:10.23919/tst.2017.8195348 · Cited by 201 · see citing works

Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection

Rigaki, M., & García, S. (2018) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2018.00019 · Cited by 176 · see citing works

Prediction of a typhoon track using a generative adversarial network and satellite images

Rüttgers, M., Lee, S.-S., Jeon, S., & You, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-42339-y · Cited by 171 · see citing works

Data driven parallel prediction of building energy consumption using generative adversarial nets

Tian, C., Li, C., Zhang, G., & Lv, Y. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.01.034 · Cited by 168 · see citing works

30th Conference on Neural Information Processing Systems (NIPS 2016)

Papamakarios, G., & Murray, I. (2016) · neural information processing systems · Cited by 164 · see citing works

Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records

Che, Z., Cheng, Y., Zhai, S., Sun, Z., & Liu, Y. (2017) · 2017 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2017.93 · Cited by 162 · see citing works

Image-To-Image Translation via Group-Wise Deep Whitening-And-Coloring Transformation

Cho, W., Choi, S., Park, D. K., Shin, I., & Choo, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01089 · Cited by 154 · cited 2× · see citing works

Detection of Coronavirus (COVID-19) Associated Pneumonia Based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model Using Chest X-ray Dataset

Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Elghamrawy, S. M. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-20601-6_22 · Cited by 151 · see citing works

Boundary-Seeking Generative Adversarial NetworksPreprint

Hjelm, R. D., Jacob, A. P., Che, T., Trischler, A., Cho, K., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08431 · Cited by 144 · see citing works

Loss-Sensitive Generative Adversarial Networks on Lipschitz DensitiesPreprint

Qi, G.-J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.06264 · Cited by 138 · see citing works

RF Sensing in the Internet of Things: A General Deep Learning Framework

Wang, X., Wang, X., & Mao, S. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1701277 · Cited by 129 · see citing works

LayoutGAN: Generating Graphic Layouts with Wireframe DiscriminatorsPreprint

Li, J., Yang, J., Hertzmann, A., Zhang, J., & Xu, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06767 · Cited by 104 · see citing works

Conditional LSTM-GAN for Melody Generation from Lyrics

Yu, Y., Srivastava, A., & Canales, S. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3424116 · Cited by 98 · see citing works

How to Train Your DRAGANPreprint

Kodali, N., Abernethy, J., Hays, J., & Kira, Z. (2017) · arXiv.org · doi:10.48550/arxiv.1705.07215 · Cited by 89 · see citing works

Unsupervised Image-to-Image Translation with Generative Adversarial NetworksPreprint

Dong, H., Neekhara, P., Wu, C., & Guo, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.02676 · Cited by 62 · see citing works

GAN-Based Key Secret-Sharing Scheme in Blockchain

Zheng, W., Wang, K., & Wang, F. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2963138 · Cited by 61 · see citing works

Detecting GAN generated Fake Images using Co-occurrence MatricesPreprint

Nataraj, L., Mohammed, T. M., Chandrasekaran, S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.06836 · Cited by 54 · see citing works

Robust Speech Recognition Using Generative Adversarial Networks

Sriram, A., Jun, H., Gaur, Y., & Satheesh, S. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462456 · Cited by 53 · see citing works

Anomaly Generation Using Generative Adversarial Networks in Host-Based Intrusion Detection

Salem, M., Taheri, S., & Yuan, J. S. (2018) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon.2018.8796769 · Cited by 50 · see citing works

An Intelligent Fuzzing Data Generation Method Based on Deep Adversarial Learning

Li, Z., Zhao, H., Shi, J., Huang, Y., & Xiong, J. (2019) · IEEE Access · doi:10.1109/access.2019.2911121 · Cited by 48 · see citing works

Improved MalGAN: Avoiding Malware Detector by Leaning Cleanware Features

Kawai, M., Ota, K., & Dong, M. (2019) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic.2019.8669079 · Cited by 47 · see citing works

Semi-Supervised Generative Adversarial Network for Gene Expression Inference

Dizaji, K. G., Wang, X., & Huang, H. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220114 · Cited by 44 · see citing works

ASM1D-GAN: An Intelligent Fault Diagnosis Method Based on Assembled 1D Convolutional Neural Network and Generative Adversarial Networks

Gao, S., Wang, X., Miao, X., Su, C., & Li, Y. (2019) · Journal of Signal Processing Systems · doi:10.1007/s11265-019-01463-8 · Cited by 44 · see citing works

Modeling Urbanization Patterns with Generative Adversarial Networks

Albert, A., Strano, E., Kaur, J., & González, M. C. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8518032 · Cited by 41 · see citing works

How to Train Your DRAGANPreprint

Kodali, N., Abernethy, J., Hays, J. H., & Kira, Z. (2017) · arXiv (Cornell University) · Cited by 40 · see citing works

Quantitatively Evaluating GANs With Divergences Proposed for TrainingPreprint

Im, D. J., Ma, H., Taylor, G. W., & Branson, K. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01045 · Cited by 40 · see citing works

Towards GAN Benchmarks Which Require GeneralizationPreprint

Gulrajani, I., Raffel, C., & Metz, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.03653 · Cited by 39 · see citing works

ARMED: How Automatic Malware Modifications Can Evade Static Detection?

Castro, R. L., Schmitt, C., & Rodosek, G. D. (2019) · International Conference on Information Management · doi:10.1109/infoman.2019.8714698 · Cited by 39 · see citing works

MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial NetworksPreprint

Dong, H.-W., Hsiao, W.-Y., Yang, L.-C., & Yang, Y.-H. (2017) · arXiv.org · doi:10.48550/arxiv.1709.06298 · Cited by 38 · see citing works

Towards Text Generation with Adversarially Learned Neural Outlines

Subramanian, S., Mudumba, S. R., Sordoni, A., Trischler, A., Courville, A. C., & Pal, C. (2018) · PolyPublie (École Polytechnique de Montréal) · doi:10.5555/3327757.3327855 · Cited by 37 · see citing works

An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks

Wang, R., Xiao, X., Guo, B., Qin, Q., & Chen, R. (2018) · Sensors · doi:10.3390/s18071985 · Cited by 31 · see citing works

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

Chen, Y., Wang, Y., Kirschen, D. S., & Zhang, B. (2019) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm40551.2019.8974096 · Cited by 29 · see citing works

Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification

Zhong, Q., Li, P., Zhang, Y., Zhou, G., & Zhu, Q. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/597 · Cited by 28 · see citing works

TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks

Liu, X., Kong, X., Liu, L., & Chiang, K. (2018) · Proceedings · doi:10.1109/icdm.2018.00149 · Cited by 27 · see citing works

MIGAN: Malware Image Synthesis Using GANs

Singh, A., Dutta, D., & Saha, A. K. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.330110033 · Cited by 25 · see citing works

Comparative Study on Generative Adversarial NetworksPreprint

Hitawala, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04271 · Cited by 24 · see citing works

Distractor Generation with Generative Adversarial Nets for Automatically Creating Fill-in-the-blank Questions

Liang, C., Yang, X., Wham, D., Pursel, B., Passonneaur, R., & Giles, C. L. (2017) · Proceedings of the Knowledge Capture Conference · doi:10.1145/3148011.3154463 · Cited by 23 · cited 2× · see citing works

Path Planning in Support of Smart Mobility Applications Using Generative Adversarial Networks

Mohammadi, M., Al‐Fuqaha, A., & Oh, J.-S. (2018) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/cybermatics_2018.2018.00168 · Cited by 23 · see citing works

Research on the Open-Categorical Classification of the Internet-of-Things Based on Generative Adversarial Networks

Zhao, C., Shi, M., Cai, Z., & Chen, C. (2018) · Applied Sciences · doi:10.3390/app8122351 · Cited by 19 · see citing works

Generative Adversarial Networks for Failure Prediction

Zheng, S., Farahat, A., & Gupta, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46133-1_37 · Cited by 19 · see citing works

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Li, T., Liu, X., & Su, S. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622140 · Cited by 18 · see citing works

Adversarial Training for Disease Prediction from Electronic Health Records with Missing DataPreprint

Hwang, U., Choi, S., Lee, H. B., & Yoon, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04126 · Cited by 16 · see citing works

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

Hu, Q., Wu, C., Wu, Y., & Xiong, N. N. (2019) · IEEE Access · doi:10.1109/access.2019.2927809 · Cited by 14 · see citing works

Learning image vegetation index through a conditional generative adversarial network

Suárez, P. L., Sappa, A. D., & Vintimilla, B. X. (2017) · IEEE Ecuador Technical Chapters Meeting (ETCM) · doi:10.1109/etcm.2017.8247538 · Cited by 12 · see citing works

Stable and improved generative adversarial nets (GANS): A constructive survey

Zhang, G., Tu, E., & Cui, D. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296606 · Cited by 12 · see citing works

Generative Adversarial Networks for Failure PredictionPreprint

Zheng, S., Farahat, A., & Gupta, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02034 · Cited by 12 · see citing works

Disease Prediction from Electronic Health Records Using Generative Adversarial Networks.Preprint

Hwang, U., Choi, S., & Yoon, S. (2017) · arXiv (Cornell University) · Cited by 11 · see citing works

Optimising Realism of Synthetic Agricultural Images using Cycle Generative Adversarial Networks

Barth, R., IJsselmuiden, J. M. M., Hemming, J., & Henten, E. J. V. (2017) · Socio-Environmental Systems Modeling · Cited by 11 · see citing works

Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical DataPreprint

Rezaei, M., Yang, H., & Meinel, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10419 · Cited by 11 · see citing works

Generative Adversarial Framework for Learning Multiple Clinical Tasks

Rezaei, M., Yang, H., & Meinel, C. (2018) · Digital Image Computing: Techniques and Applications · doi:10.1109/dicta.2018.8615772 · Cited by 11 · see citing works

Technology Computer Aided Design Study of GaN MISFET With Double P-Buried Layers

Wang, Y., Bao, M.-T., Cao, F., Tang, J., & Luo, X. (2019) · IEEE Access · doi:10.1109/access.2019.2924999 · Cited by 9 · see citing works

Oil Price Forecasting Using Supervised GANs with Continuous Wavelet Transform Features

Luo, Z.-J., Chen, J.-H., Cai, X.-J., Tanaka, K., Takiguchi, T., Kinkyo, T., & Hamori, S. (2018) · International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8546240 · Cited by 8 · see citing works

Typhoon track prediction using satellite images in a Generative Adversarial NetworkPreprint

Rüttgers, M., Lee, S.-S., & You, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.05382 · Cited by 6 · see citing works

TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial NetworksPreprint

Liu, X., Kong, X., Liu, L., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07582 · Cited by 6 · see citing works

Using Generative Adversarial Networks to Develop a Realistic Human Behavior Simulator

el Hassouni, A., Hoogendoorn, M., & Muhonen, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03098-8_32 · Cited by 5 · see citing works

Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks

Popperli, M., Gulagundi, R., Yogamani, S. K., & Milz, S. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8814091 · Cited by 3 · see citing works

Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with a Conditional Generative Adversarial NetworkPreprint

Yuan, T. (2019) · arXiv (Cornell University) · doi:10.13016/m2pqan-zsbk · Cited by 1 · see citing works

Non-uniformity correction for photon-counting detectors using double GANs

Fang, W., & Li, L. C. (2019) · doi:10.1117/12.2534818 · Cited by 1 · see citing works

Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial NetworksPreprint

Pöpperl, M., Gulagundi, R., Yogamani, S. K., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09842

Deep Learning in Multimodal Fusion for Sustainable Plant Care: A Comprehensive Review

Yang, Z., Li, Y., Wang, R.-F., Hu, P., & Su, W. (2025) · Sustainability · doi:10.3390/su17125255 · Cited by 83 · see citing works

RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization

Hu, J., Chen, J., Xue, C., Liang, P., Xiang, Y., Zhang, C., Chi, X., Liu, G., Ye, Y., Cui, D., et al. (2024) · Light Science & Applications · doi:10.1038/s41377-024-01394-5 · Cited by 74 · see citing works

An Effective Cost-Sensitive XGBoost Method for Malicious URLs Detection in Imbalanced Dataset

He, S., Li, B., Peng, H., Xin, J., & Zhang, E. (2021) · IEEE Access · doi:10.1109/access.2021.3093094 · Cited by 61 · see citing works

Application of Deep Learning for Weapons Detection in Surveillance Videos

Hashmi, T. S. S., Haq, N. U., Fraz, M. M., & Shahzad, M. (2021) · International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt252288.2021.9441523 · Cited by 60 · see citing works

Artificial intelligence generates novel 3D printing formulations

Elbadawi, M., Li, H., Sun, S., Alkahtani, M. E., Basit, A. W., & Gaisford, S. (2024) · Applied Materials Today · doi:10.1016/j.apmt.2024.102061 · Cited by 52 · see citing works

Generative adversarial networks for biomedical time series forecasting and imputation

Festag, S., Denzler, J., & Spreckelsen, C. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104058 · Cited by 46 · see citing works

Literature survey of multi-track music generation model based on generative confrontation network in intelligent composition

Liu, W. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04914-5 · Cited by 39 · see citing works

Medical multivariate time series imputation and forecasting based on a recurrent conditional Wasserstein GAN and attention

Festag, S., & Spreckelsen, C. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104320 · Cited by 27 · see citing works

Application of generative adversarial networks in image, face reconstruction and medical imaging: challenges and the current progress

Sabnam, S., & Rajagopal, S. (2024) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2024.2330524 · Cited by 21 · see citing works

Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks

Wang, J., & Chen, Z. (2024) · PLoS ONE · doi:10.1371/journal.pone.0306094 · Cited by 18 · see citing works

Construction of Sports Training Performance Prediction Model Based on a Generative Adversarial Deep Neural Network Algorithm

Li, G. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1211238 · Cited by 13 · see citing works

Development of a biofeedback system using harmonic musical intervals to control heart rate variability with a generative adversarial network

Idrobo-Ávila, E., Correa, H. L., Muñoz-Bolaños, F., van Noorden, L., & Vargas-Cañas, R. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103095 · Cited by 15 · see citing works

Generative AI in Marketing: Foundations, Trends, and Future Research Propositions

Prasanna, A., & Kushwaha, B. P. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/5542513 · Cited by 10 · see citing works

Research on cross-domain generative diagnosis for oil and gas pipeline defect based on limited field data

Yao, J., Liang, W., & Xiao, Z. (2025) · Energy · doi:10.1016/j.energy.2025.135086 · Cited by 8 · see citing works

Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine

Dang, M., Liu, B., Chen, Y., & Zhang, Z. (2026) · Psychoradiology · doi:10.1093/psyrad/kkag007 · Cited by 7 · see citing works

Facial identity recognition using StyleGAN3 inversion and improved tiny YOLOv7 model

Kumar, A., Bhattacharjee, S., Kumar, A., & Jayakody, D. N. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-93096-0 · Cited by 8 · see citing works

Integrating statistical design and inference: A roadmap for robust and trustworthy medical AI

Wei, Q., Cui, M., Liu, Z., Liu, Z., Feng, G., Li, Y., Christiani, D. C., Li, L., Wang, J., Hao, Y., et al. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100145 · Cited by 6 · see citing works

Map geographic information road extraction method based on generative adversarial network and U-Net

Liu, G., He, H., Gao, Y., Zhang, G., & Cao, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-10979-y · Cited by 5 · see citing works

Exploring Artificial Intelligence Programs’ Understanding of Lens, Cataract, and Refractive Surgery Information

Şensoy, E., & Çıtırık, M. (2023) · Middle East African Journal of Ophthalmology · doi:10.4103/meajo.meajo_199_23 · Cited by 5 · see citing works

Advancements and Challenges in AI Applications for B2C Retail Promotions

Jordan, S., Kracklauer, A. H., & Germelmann, C. C. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-76365-6_1 · Cited by 3 · see citing works

Reconstructing spectral shapes with GAN models: A data-driven approach for high-resolution spectra from low-resolution spectrometers

Tai, M.-H., & Hsu, C. (2025) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2025.105333 · Cited by 3 · see citing works

An Efficient Adversarial Defiance Towards Malware Detection System (MDS)

Rizvi, S. K. J., & Fraz, M. M. (2022) · IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) · doi:10.1109/honet56683.2022.10019076 · Cited by 3 · see citing works

NLP Meets Vision for Visual Interpretation - A Retrospective Insight and Future directions

Jamshed, A., & Fraz, M. M. (2021) · International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt252288.2021.9441517 · Cited by 3 · see citing works

Integrating post occupancy evaluation and energy performance metrics using conditional generative adversarial networks for intelligent school design

Ilbeigi, M., Ghomeishi, M., Asgharzadeh, A., & Amani, N. (2025) · Discover Sustainability · doi:10.1007/s43621-025-02523-9 · Cited by 2 · see citing works

Robust malware clustering of windows portable executables using ensemble latent representation and distribution modeling

Rizvi, S. K. J., & Fraz, M. M. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7621 · Cited by 2 · see citing works

Deep Neural Network Based Anomaly Detection For Smart Surveillance System

Shubhangi, D. C., Waheed, M. A., Gadgay, B., Vishwanath, P., & Tadalapur, V. (2022) · IEEE North Karnataka Subsection Flagship International Conference (NKCon) · doi:10.1109/nkcon56289.2022.10126698 · Cited by 2 · see citing works

A GAN-CNN Fusion Framework for Deep Learning-Based DOA Estimation in Low-SNR Environments

Zhang, Z., Xu, W., Zou, H., & Yi, S. (2026) · Sensors · doi:10.3390/s26051676 · Cited by 1 · see citing works

HunarmandAI: A Bilingual Intelligent Diagnostic Interface for Smart Vehicles and Industrial Mechatronic Systems

Nabeel, M., Khan, A., Rizwan, M. D., Malik, M., & Fraz, M. M. (2026) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai70912.2026.11551944 · Cited by 1 · see citing works

An optimized generalized adversarial system for predicting specific substructures in brainstem

Srikanth, B., Jayaprada, S., Kumar, K. K., Chaduvula, K., Markapudi, B. R., & Khasim, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13663-9 · Cited by 1 · see citing works

Deep Generative Adversarial Network-Based MRI Slices Reconstruction and Enhancement for Alzheimer’s Stages Classification

Shankar, V. G., & Sisodia, D. S. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-46341-9_3 · Cited by 1 · see citing works

Unveiling Its Origins, Principles, and Technological Underpinnings

Dehankar, P., & Das, S. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-3691-5.ch001

The Attack and Defense Researches on the Dual‐Layer Network of Multivariable Anomaly Causes

Han, J., Zhang, R., Ye, Z., Huo, X., Xiao, Y., & Zheng, Y. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/1522150

Harnessing the Power of Generative Adversarial Networks for American Sign Language Generation

Mahajan, S., Patil, A., Menpara, J., Nandhini, K., Gite, S., & Pradhan, B. (2026) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-032-05507-1_30

Machine Learning for Data Mining in Plant Research

Hurrah, I. M., Banday, L., Din, T. M. U., Rasool, I., & Mandal, S. (2026) · CABI biotechnology series. · doi:10.1079/9781800628649.0011

Detection of AI-Generated Images Using EfficientNet: A Step Toward Computational Efficiency

Shafiq, M. T., & Pratap, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7526-5_23

The application of generative adversarial networks (GANs) as new approach methods (NAMs) toward nonanimal testing

Chen, X., Li, T., Tong, W., & Chen, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26514-3.00019-9

SpecMCR: A multi-channel spectral regression framework with clustering and GAN-based augmentation for accurate grape sugar prediction

Gao, J., Ding, X., Gong, H., Zhongzhi, H., & Guo, B. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112456

Survey on Generative Adversarial Behavior in Artificial Neural Tasks

Qamar, R., Bajao, N. A., Suwarno, I., & Jokhio, F. A. (2022) · Iraqi Journal for Computer Science and Mathematics · doi:10.52866/ijcsm.2022.02.01.009 · Cited by 11 · see citing works

A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing

Dash, A., Ye, J., & Wang, G. (2023) · IEEE Access · doi:10.1109/access.2023.3346273 · Cited by 235 · see citing works

173Chapter 10 GANs: applications and use cases

Abbasi, S., & Rahmani, A. M. (2025) · De Gruyter eBooks · doi:10.1515/9783111354934-010

Generative Adversarial Networks: a systematic review and applications

Asimopoulos, D. C., Nitsiou, M., Lazaridis, L., & Fragulis, G. F. (2022) · SHS Web of Conferences · doi:10.1051/shsconf/202213903012 · Cited by 12 · see citing works

Generative Adversarial Networks (GANs): Principles, Development, Challenges, and Diverse Applications

Sun, W. (2025) · Applied and Computational Engineering · doi:10.54254/2755-2721/2025.ld25482 · Cited by 2 · see citing works