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How Generative Adversarial Networks and Their Variants Work: An Overview
Open accessAbstract
Generative Adversarial Networks (GANs) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property allows GANs to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation, and other academic fields. In this article, we discuss the details of GANs for those readers who are familiar with, but do not comprehend GANs deeply or who wish to view GANs from various perspectives. In addition, we explain how GANs operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GANs for their research.
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Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,703
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
(2017) · International Conference on Machine Learning · Cited by 6,382
Image Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025
“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
(1999) · Cited by 5,147
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
Trust region policy optimization
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578
Spectral Normalization for Generative Adversarial Networks
Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018) · arXiv · doi:10.48550/arxiv.1802.05957 · Cited by 3,438
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
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., & Darrell, T. (2017) · arXiv · doi:10.48550/arxiv.1711.03213 · Cited by 2,656
De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(99)00047-7 · Cited by 2,325
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Yu, L., Zhang, W., Wang, J., & Yu, Y. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10804 · Cited by 2,285
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Yi, Z., Zhang, H., Tan, P., & Gong, M. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.310 · Cited by 2,134
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882269 · Cited by 2,020
Generative adversarial imitation learning
Ho, J., & Ermon, S. (2016) · arXiv · doi:10.48550/arxiv.1606.03476 · Cited by 1,942
Maximum Entropy Inverse Reinforcement Learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2018) · doi:10.1184/r1/6555512 · Cited by 1,869
Conditional Image Synthesis With Auxiliary Classifier GANs
Odena, A., Olah, C., & Shlens, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09585 · Cited by 1,837
Learning from Simulated and Unsupervised Images through Adversarial Training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.241 · Cited by 1,814
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882269 · Cited by 1,773
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Kim, T., Cha, M., Kim, H., Lee, J. K., & Kim, J. (2017) · arXiv · doi:10.48550/arxiv.1703.05192 · Cited by 1,716
Information processing in dynamical systems: foundations of harmony theory
Smolensky, P. (1986) · The MIT Press eBooks · Cited by 1,671
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.18 · Cited by 1,603
Tutorial on Variational Autoencoders
Doersch, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05908 · Cited by 1,470
Pixel Recurrent Neural Networks
van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.06759 · Cited by 1,437
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Wu, J., Zhang, C., Xue, T., Freeman, W. T., & Tenenbaum, J. B. (2016) · arXiv · doi:10.48550/arxiv.1610.07584 · Cited by 1,393 · cited 1×
“Their capability to represent complex and high-dimensional data can be utilized in treating images [2, 12, 57, 65, 127, 133, 145], videos [122, 125, 126], music generation [41, 66, 141], natural languages [48, 73] and other academic domains such as medical images [16, 77, 136] and security [109, 124].”
BackgroundContinual Learning with Deep Generative Replay
Shin, H., Lee, J. K., Kim, J., & Kim, J. (2017) · arXiv · doi:10.48550/arxiv.1705.08690 · Cited by 1,385
A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
Rosenblatt, M. (1956) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.42.1.43 · Cited by 1,365
Autoencoding beyond pixels using a learned similarity metric
Larsen, A., Sønderby, S. K., Larochelle, H., & Winther, O. (2016) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 1,317
NIPS 2016 Tutorial: Generative Adversarial Networks
Goodfellow, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00160 · Cited by 1,229
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Nowozin, S., Cseke, B., & Tomioka, R. (2016) · arXiv · doi:10.48550/arxiv.1606.00709 · Cited by 1,183
A guide to convolution arithmetic for deep learning
Dumoulin, V., & Visin, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07285 · Cited by 1,172
Jointly Embedding Multiple Single-Cell Omics Measurements
Liu, J., Huang, Y., Singh, R., Vert, J., & Noble, W. S. (2019) · Leibniz international proceedings in informatics · doi:10.4230/lipics.wabi.2019.10 · Cited by 1,141
MoCoGAN: Decomposing Motion and Content for Video Generation
Tulyakov, S., Liu, M.-Y., Yang, X., & Kautz, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00165 · Cited by 1,076
A method for enforcing integrability in shape from shading algorithms
Frankot, R. T., & Chellappa, R. (1988) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.3909 · Cited by 1,038
Perceptual Generative Adversarial Networks for Small Object Detection
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.211 · Cited by 953 · cited 1×
“Notably, Li et al. [69] tries to transform a small object with low resolution into a super resolved large object to make the object more discriminative.”
BackgroundEnergy-based Generative Adversarial Network
Zhao, J., Mathieu, M., & LeCun, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.03126 · Cited by 912 · cited 6×
“Details BEGAN [10] L Wasserstein distance between loss distributions EBGAN [143] Margin m is adjusted in EBGAN’s training Progressive GAN [56] succeeds in to generating high resolution images in a single GAN, making training faster and more stable.”
BackgroundMethodInfluentialThe Distribution of Chi-Square
Wilson, E. B., & Hilferty, M. M. (1931) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.17.12.684 · Cited by 824
Generating Videos with Scene Dynamics
Vondrick, C., Pirsiavash, H., & Torralba, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.02612 · Cited by 785
Barratt, S., & Sharma, R. (2018) · arXiv · doi:10.48550/arxiv.1801.01973 · Cited by 732
Attribute2Image: Conditional Image Generation from Visual Attributes
Yan, X., Yang, J., Sohn, K., & Lee, H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_47 · Cited by 688
Huang, R., Zhang, S., Li, T., & He, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.267 · Cited by 687
BEGAN: Boundary Equilibrium Generative Adversarial Networks
Berthelot, D., Schumm, T., & Metz, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10717 · Cited by 654
Donahue, J., Krähenbühl, P., & Darrell, T. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.09782 · Cited by 616
SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
Xue, Y., Xu, T., Zhang, H., Long, L. R., & Huang, X. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9377-x · Cited by 605
Autoencoding beyond pixels using a learned similarity metric
Larsen, A., Sønderby, S. K., Larochelle, H., & Winther, O. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.09300 · Cited by 599
Salimans, T., Karpathy, A., Chen, X., & Kingma, D. P. (2017) · arXiv · doi:10.48550/arxiv.1701.05517 · Cited by 584
Adversarially Learned Inference
Dumoulin, V., Belghazi, M. I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., & Courville, A. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00704 · Cited by 579
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., & Yosinski, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.374 · Cited by 578
VITAL: VIsual Tracking via Adversarial Learning
Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C., Lau, R. W. H., & Yang, M.-H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00937 · Cited by 577
Invertible Conditional GANs for image editing
Perarnau, G., van de Weijer, J., Raducanu, B., & Álvarez, J. M. (2016) · arXiv · doi:10.48550/arxiv.1611.06355 · Cited by 563
Generative Moment Matching Networks
Li, Y., Swersky, K., & Zemel, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.02761 · Cited by 517
Face aging with conditional generative adversarial networks
Antipov, G., Baccouche, M., & Dugelay, J.-L. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296650 · Cited by 515
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., & Sutton, C. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07761 · Cited by 470
Generating Videos with Scene Dynamics
Vondrick, C., Pirsiavash, H., & Torralba, A. (2016) · Maryland Shared Open Access Repository · doi:10.13016/m26gih-tnyz · Cited by 441
Stacked Generative Adversarial Networks
Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.202 · Cited by 434
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Guimaraes, G. L., Sánchez-Lengeling, B., Outeiral, C., Farias, P. L. C., & Aspuru‐Guzik, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10843 · Cited by 413
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Mogren, O. (2016) · arXiv · doi:10.48550/arxiv.1611.09904 · Cited by 386
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Li, C., Chang, W.-C., Cheng, Y., Yang, Y., & Póczos, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08584 · Cited by 384
Unrolled Generative Adversarial Networks
Metz, L., Poole, B., Pfau, D., & Sohl‐Dickstein, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02163 · Cited by 382
cGANs with Projection Discriminator
Miyato, T., & Koyama, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.05637 · Cited by 382
Perceptual Adversarial Networks for Image-to-Image Transformation
Wang, C., Xu, C., Wang, C., & Tao, D. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2836316 · Cited by 381
A DIRT-T Approach to Unsupervised Domain Adaptation
Shu, R., Bui, H., Narui, H., & Ermon, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08735 · Cited by 377
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
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
Qi, G.-J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01265-2 · Cited by 341
The Pose Knows: Video Forecasting by Generating Pose Futures
Walker, J., Marino, K., Gupta, A., & Hebert, M. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.361 · Cited by 330
Hsu, C.-C., Hwang, H.-T., Wu, Y.-C., Tsao, Y., & Wang, H.-M. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-63 · Cited by 320
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
Springenberg, J. T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06390 · Cited by 300
Multi-agent Diverse Generative Adversarial Networks
Ghosh, A., Kulharia, V., Namboodiri, V., Torr, P. H. S., & Dokania, P. K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00888 · Cited by 295
Fenchel, W. (2014) · Traces and Emergence of Nonlinear Programming · doi:10.1007/978-3-0348-0439-4_7 · Cited by 285
3D Shape Induction from 2D Views of Multiple Objects
Gadelha, M., Maji, S., & Wang, R. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00053 · Cited by 260
One-Sided Unsupervised Domain MappingPreprint
Benaim, S., & Wolf, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00826 · Cited by 257
Generating images with recurrent adversarial networksPreprint
Im, D. J., Kim, C. D., Jiang, H., & Memisevic, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.05110 · Cited by 233
SSGAN: Secure Steganography Based on Generative Adversarial Networks
Shi, H., Dong, J., Wang, W., Qian, Y., & Zhang, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-77380-3_51 · Cited by 230
On integral probability metrics, ϕ-divergences and binary classificationPreprint
Sriperumbudur, B. K., Fukumizu, K., Gretton, A., Schölkopf, B., & Lanckriet, G. R. G. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0901.2698 · Cited by 224
Steganographic generative adversarial networks
Volkhonskiy, D., Nazarov, I., & Burnaev, E. (2020) · Twelfth International Conference on Machine Vision (ICMV 2019) · doi:10.1117/12.2559429 · Cited by 224
The Cramer Distance as a Solution to Biased Wasserstein Gradients
Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., & Munos, R. (2017) · arXiv · doi:10.48550/arxiv.1705.10743 · Cited by 223
Variational Approaches for Auto-Encoding Generative Adversarial Networks
Rosca, M., Lakshminarayanan, B., Warde-Farley, D., & Mohamed, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04987 · Cited by 217
Mode Regularized Generative Adversarial Networks
Che, T., Li, Y., Jacob, A. P., Bengio, Y., & Li, W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02136 · Cited by 204
Triple Generative Adversarial Nets
Li, C., Xu, K., Zhu, J., & Zhang, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.02291 · Cited by 204
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic LayoutsPreprint
Karacan, L., Akata, Z., Erdem, A., & Erdem, E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00215 · Cited by 200
GP-GAN: Towards Realistic High-Resolution Image Blending
Wu, H., Zheng, S., Zhang, J., & Huang, K. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3350944 · Cited by 200
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 191
Finn, C., Christiano, P. F., Abbeel, P., & Levine, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.03852 · Cited by 185
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Arora, S., Ge, R., Liang, Y., Ma, T., & Zhang, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00573 · Cited by 182
Semi-Supervised Learning with Context-Conditional Generative Adversarial NetworksPreprint
Denton, E., Gross, S., & Fergus, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06430 · Cited by 179
Q-PrOP: Sample-efficient policy gradient with an off-policy critic
Gu, S., Lillicrap, T., Ghahramani, Z., Turner, R. E., & Levine, S. (2016) · arXiv (Cornell University) · doi:10.17863/cam.21294 · Cited by 174
Connecting Generative Adversarial Networks and Actor-Critic MethodsPreprint
Pfau, D., & Vinyals, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.01945 · Cited by 171
SeGAN: Segmenting and Generating the Invisible
Ehsani, K., Mottaghi, R., & Farhadi, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00643 · Cited by 170
Deep Generative Adversarial Networks for Compressed Sensing Automates MRIPreprint
Mardani, M., Gong, E., Cheng, J. Y., Vasanawala, S., Zaharchuk, G., Alley, M., Thakur, N., Han, S., Dally, W., Pauly, J., et al. (2017) · arXiv.org · doi:10.48550/arxiv.1706.00051 · Cited by 164
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Che, T., Li, Y., Zhang, R., Hjelm, R. D., Li, W., Song, Y., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.07983 · Cited by 163
Representation Learning by Rotating Your Faces
Tran, L., Yin, X., & Liu, X. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2868350 · Cited by 158
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial NetworkPreprint
Dash, A., Gamboa, J. C. B., Ahmed, S., Liwicki, M., & Afzal, M. Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06412 · Cited by 156
Sample Complexity of Testing the Manifold Hypothesis
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Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max OptimizationPreprint
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Kantorovich-Rubinstein norm and its application in the theory of Lipschitz spaces
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Self-citation
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Metadata
- Authors
- Hong, Y., Hwang, U., Yoo, J., & Yoon, S.
- Venue
- ACM Computing Surveys
- Published
- 2019-02-13
- Volume
- 52
- Issue
- 1
- Pages
- 1-43
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2899901572
- Cited by
- 373
- References
- 138
- FWCI
- 25.03
- Type
- journal article
- Language
- en
- Open access
- green
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 ›
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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.
