The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Generative Adversarial Networks
Abstract
The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
Cited by
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021) · arXiv · doi:10.48550/arxiv.2102.12092 · Cited by 2,417
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770
NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1410.8516 · Cited by 1,493
A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
Training Generative Adversarial Networks with Limited Data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., & Aila, T. (2020) · arXiv · doi:10.48550/arxiv.2006.06676 · Cited by 1,170
A Survey of Quantization Methods for Efficient Neural Network Inference
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., & Keutzer, K. (2022) · Low-Power Computer Vision · doi:10.1201/9781003162810-13 · Cited by 1,219
Convolutional neural network: a review of models, methodologies and applications to object detection
Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119
Resolution-robust Large Mask Inpainting with Fourier Convolutions
Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., & Lempitsky, V. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00323 · Cited by 1,123
Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046
Deep learning in cancer diagnosis, prognosis and treatment selection
Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00968-x · Cited by 932
Addressing bias in big data and AI for health care: A call for open science
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100347 · Cited by 903
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2021) · arXiv · doi:10.48550/arxiv.2109.06977 · Cited by 635
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Liu, G., Tao, A., Kautz, J., & Catanzaro, B. (2018) · arXiv · doi:10.48550/arxiv.1808.06601 · Cited by 594
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501825 · Cited by 639
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
CogView: Mastering Text-to-Image Generation via Transformers
Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., et al. (2021) · arXiv · doi:10.48550/arxiv.2105.13290 · Cited by 450
Improved Precision and Recall Metric for Assessing Generative Models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., & Aila, T. (2019) · arXiv · doi:10.48550/arxiv.1904.06991 · Cited by 379
Mixed supervision for surface-defect detection: From weakly to fully supervised learning
Božič, J., Tabernik, D., & Skočaj, D. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103459 · Cited by 377
The unreasonable effectiveness of deep learning in artificial intelligence
Sejnowski, T. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907373117 · Cited by 352
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
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., & Saynisch-Wagner, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00374-3 · Cited by 308
A Review on Deep Learning Techniques for Video Prediction
Oprea, S., Martinez-Gonzalez, P., Garcia-Garcia, A., Castro-Vargas, J. A., Orts-Escolano, S., Garcia-Rodriguez, J., & Argyros, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3045007 · Cited by 301
Advhat: Real-world adversarial attack on arcface face id system
Komkov, S., & Petiushko, A. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412236 · Cited by 294
Automatic multiorgan segmentation in thorax CT images using U‐net‐ GAN
Dong, X., Lei, Y., Wang, T., Thomas, M., Tang, L., Curran, W. J., Liu, T., & Yang, X. (2019) · Medical Physics · doi:10.1002/mp.13458 · Cited by 275
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., & Biswas, R. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab859 · Cited by 274
Defect Image Sample Generation With GAN for Improving Defect Recognition
Niu, S., Li, B., Wang, X., & Lin, H. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.2967415 · Cited by 251
A Survey on Semi-, Self- and Unsupervised Learning for Image Classification
Schmarje, L., Santarossa, M., Schroder, S.-M., & Koch, R. (2021) · IEEE Access · doi:10.1109/access.2021.3084358 · Cited by 246
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
Vowels, M. J., Camgoz, N. C., & Bowden, R. (2023) · ACM Computing Surveys · doi:10.1145/3527154 · Cited by 237
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
Liu, M.-Y., Huang, X., Yu, J., Wang, T.-C., & Mallya, A. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3049196 · Cited by 236
A survey of swarm and evolutionary computing approaches for deep learning
Darwish, A., Hassanien, A. E., & Das, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09719-2 · Cited by 200
Machine learning for biochemical engineering: A review
Mowbray, M., Savage, T., Wu, C., Song, Z., Cho, B. A., Del Rio-Chanona, E. A., & Zhang, D. (2021) · Biochemical Engineering Journal · doi:10.1016/j.bej.2021.108054 · Cited by 188
Learning to generate images with perceptual similarity metrics
Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., & Zemel, R. S. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8297089 · Cited by 186
MTD-Net: Learning to Detect Deepfakes Images by Multi-Scale Texture Difference
Yang, J., Li, A., Xiao, S., Lu, W., & Gao, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3102487 · Cited by 184
Review on self-supervised image recognition using deep neural networks
Ohri, K., & Kumar, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107090 · Cited by 179
The Advent of Generative Chemistry
Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A. (2020) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00088 · Cited by 151
Deep learning for in vivo near-infrared imaging
Ma, Z., Wang, F., Wang, W., Zhong, Y., & Dai, H. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021446118 · Cited by 135
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119
FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset
Khalid, H., Tariq, S., Kim, M., & Woo, S. S. (2021) · arXiv · doi:10.48550/arxiv.2108.05080 · Cited by 116
A Survey of Self-Supervised and Few-Shot Object Detection
Huang, G., Laradji, I., Vazquez, D., Lacoste-Julien, S., & Rodriguez, P. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3199617 · Cited by 100
Ueltzhöffer, K. (2018) · Biological Cybernetics · doi:10.1007/s00422-018-0785-7 · Cited by 98
Structure-based protein design with deep learning
Ovchinnikov, S., & Huang, P.-S. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.08.004 · Cited by 93
Deep Video Anomaly Detection: Opportunities and Challenges
Ren, J., Xia, F., Liu, Y., & Lee, I. (2021) · 2021 International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw53433.2021.00125 · Cited by 84
Painting with baryons: augmenting N-body simulations with gas using deep generative models
Tröster, T., Ferguson, C., Harnois-Déraps, J., & McCarthy, I. G. (2019) · Monthly Notices of the Royal Astronomical Society: Letters · doi:10.1093/mnrasl/slz075 · Cited by 77
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis
Peng, C., Lin, W.-A., Liao, H., Chellappa, R., & Zhou, S. K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00777 · Cited by 75
Super-resolution emulator of cosmological simulations using deep physical models
Kodi Ramanah, D., Charnock, T., Villaescusa-Navarro, F., & Wandelt, B. D. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1428 · Cited by 70
Barragán-Montero, A. M., Thomas, M., Defraene, G., Michiels, S., Haustermans, K., Lee, J. A., & Sterpin, E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.026 · Cited by 69
Zanisi, L., Huertas-Company, M., Lanusse, F., Bottrell, C., Pillepich, A., Nelson, D., Rodriguez-Gomez, V., Shankar, F., Hernquist, L., Dekel, A., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3864 · Cited by 65
Detecting outliers in astronomical images with deep generative networks
Margalef-Bentabol, B., Huertas-Company, M., Charnock, T., Margalef-Bentabol, C., Bernardi, M., Dubois, Y., Storey-Fisher, K., & Zanisi, L. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1647 · Cited by 56
The PAU Survey: Photometric redshifts using transfer learning from simulations
Eriksen, M., Alarcon, A., Cabayol, L., Carretero, J., Casas, R., Castander, F. J., De Vicente, J., Fernandez, E., Garcia-Bellido, J., Gaztanaga, E., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2265 · Cited by 39
Synthetic Sensor Data for Human Activity Recognition
Alharbi, F., Ouarbya, L., & Ward, J. A. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206624 · Cited by 40
XIRL: Cross-embodiment inverse reinforcement learning
Zakka, K., Zeng, A., Florence, P., Tompson, J., Bohg, J., & Dwibedi, D. (2021) · arXiv · doi:10.48550/arxiv.2106.03911 · Cited by 38
Dynamical mass inference of galaxy clusters with neural flows
Kodi Ramanah, D., Wojtak, R., Ansari, Z., Gall, C., & Hjorth, J. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2886 · Cited by 37
Cunningham, R. J., & Loram, I. D. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0715 · Cited by 36
Chianese, M., Coogan, A., Hofma, P., Otten, S., & Weniger, C. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1477 · Cited by 36
Rajotte, J.-F., Mukherjee, S., Robinson, C., Ortiz, A., West, C., Ferres, J. M. L., & Ng, R. T. (2021) · Proceedings of the Conference on Information Technology for Social Good · doi:10.1145/3462203.3475875 · Cited by 34
Nonlinear 3D cosmic web simulation with heavy-tailed generative adversarial networks
Feder, R. M., Berger, P., & Stein, G. (2020) · Physical Review D · doi:10.1103/physrevd.102.103504 · Cited by 30
A unified generative model using generative adversarial network for activity recognition
Chan, M. H., & Noor, M. H. M. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02548-0 · Cited by 27
Bernardini, M., Feldmann, R., Anglés-Alcázar, D., Boylan-Kolchin, M., Bullock, J., Mayer, L., & Stadel, J. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3088 · Cited by 26
Stark, D., Launet, B., Schawinski, K., Zhang, C., Koss, M., Turp, M. D., Sartori, L. F., Zhang, H., Chen, Y., & Weigel, A. K. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty764 · Cited by 24
End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning
Feldman, T., & Peake, A. (2021) · arXiv · doi:10.48550/arxiv.2104.02532 · Cited by 19
Fair Machine Learning in Healthcare: A Survey
Feng, Q., Du, M., Zou, N., & Hu, X. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3361836 · Cited by 12
Nonstandard Periodic Gait Energy Image for Gait Recognition and Data Augmentation
Wang, K., Liu, L., Lee, Y., Ding, X., & Lin, J. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-31723-2_17 · Cited by 12
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
Lanusse, F., Melchior, P., & Moolekamp, F. (2019) · arXiv · doi:10.48550/arxiv.1912.03980 · Cited by 9
Autoencoding sensory substitution
Tóth, V., & Parkkonen, L. (2019) · Unpublished · doi:10.13140/rg.2.2.10576.87048 · Cited by 7
Visual Conceptual Blending with Large-scale Language and Vision Models
Ge, S., & Parikh, D. (2021) · arXiv · doi:10.48550/arxiv.2106.14127 · Cited by 4
Conversational AI Systems for Social Good: Opportunities and Challenges
Qi, P., Huang, J., Wu, Y., He, X., & Zhou, B. (2021) · arXiv · doi:10.48550/arxiv.2105.06457 · Cited by 3
Metadata
- Venue
- The Science of Deep Learning
- Publisher
- Cambridge University Press
- Published
- 2022-08-18
- Pages
- 153-173
- OpenAlex
- W1710476689
- Cited by
- 2,402
- References
- 5
- FWCI
- 89.93
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
