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Papers and Websites
• Semantic Image Synthesis with Spatially-Adaptive Normalization (Park, Liu, Wang, and Zhu, 2019): https://arxiv.org/abs/1903.07291
• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (Ledig et al., 2017): https://arxiv.org/abs/1609.04802
• Multimodal Unsupervised Image-to-Image Translation (Huang et al., 2018): https://github.com/NVlabs/MUNIT
• StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks (Zhang et al., 2017): https://arxiv.org/abs/1612.03242
• Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233
• Snapchat: https://www.snapchat.com
• MaskGAN: Towards Diverse and Interactive Facial Image Manipulation (Lee, Liu, Wu, and Luo, 2020): https://arxiv.org/abs/1907.11922
• When AI generated paintings dance to music... (2019): https://www.youtube.com/watch?v=85l961MmY8Y
• Data Augmentation Generative Adversarial Networks (Antoniou, Storkey, and Edwards, 2018): https://arxiv.org/abs/1711.04340
• Training progression of StyleGAN on H&E tissue fragments (Zhou, 2019): https://twitter.com/realSharonZhou/status/1182877446690852867
• Establishing an evaluation metric to quantify climate change image realism (Sharon Zhou, Luccioni, Cosne, Bernstein, and Bengio, 2020): https://iopscience.iop.org/article/10.1088/2632-2153/ab7657/meta
• Deepfake example (2019): https://en.wikipedia.org/wiki/File:Deepfake_example.gif
• Introduction to adversarial robustness (Kolter and Madry): https://adversarial-ml-tutorial.org/introduction/
• Large Scale GAN Training for High Fidelity Natural Image Synthesis (Brock, Donahue, and Simonyan, 2019): https://openreview.net/pdf?id=B1xsqj09Fm
• GazeGAN - Unpaired Adversarial Image Generation for Gaze Estimation (Sela, Xu, He, Navalpakkam, and Lagun, 2017): https://arxiv.org/abs/1711.09767
• Data Augmentation using GANs for Speech Emotion Recognition (Chatziagapi et al., 2019): https://pdfs.semanticscholar.org/395b/ea6f025e599db710893acb6321e2a1898a1f.pdf
• GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification (Frid-Adar et al., 2018): https://arxiv.org/abs/1803.01229
• GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation (Bowles, Gunn, Hammers, and Rueckert, 2018): https://arxiv.org/abs/1811.10669
• Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks (Sandfort, Yan, Pickhardt, and Summers, 2019): https://www.nature.com/articles/s41598-019-52737-
x/figures/3
• De-identification without losing faces (Li and Lyu, 2019): https://arxiv.org/abs/1902.04202
• Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (Beaulieu-Jones et al., 2019): https://www.ahajournals.org/doi/epub/10.1161/CIRCOUTCOMES.118.005122
• DeepPrivacy: A Generative Adversarial Network for Face Anonymization (Hukkelås, Mester, and Lindseth, 2019): https://arxiv.org/abs/1909.04538
• GAIN: Missing Data Imputation using Generative Adversarial Nets (Yoon, Jordon, and van der Schaar, 2018): https://arxiv.org/abs/1806.02920
• Conditional Infilling GANs for Data Augmentation in Mammogram Classification (E. Wu, K. Wu, Cox, and Lotter, 2018): https://link.springer.com/chapter/10.1007/978-3-030-00946-5_11
• The Effectiveness of Data Augmentation in Image Classification using Deep Learning (Perez and Wang, 2017): https://arxiv.org/abs/1712.04621
• CIFAR-10 and CIFAR-100 Dataset; Learning Multiple Layers of Features from Tiny Images (Krizhevsky, 2009): https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf
• DeOldify... (Antic, 2019): https://twitter.com/citnaj/status/1124904251128406016
• pix2pixHD (Wang et al., 2018): https://github.com/NVIDIA/pix2pixHD
• [4k, 60 fps] Arrival of a Train at La Ciotat (The Lumière Brothers, 1896) (Shiryaev, 2020): https://youtu.be/3RYNThid23g
• Image-to-Image Translation with Conditional Adversarial Networks (Isola, Zhu, Zhou, and Efros, 2018): https://arxiv.org/abs/1611.07004
• Pose Guided Person Image Generation (Ma et al., 2018): https://arxiv.org/abs/1705.09368
• AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks (Xu et al., 2017): https://arxiv.org/abs/1711.10485
• Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233
• Patch-Based Image Inpainting with Generative Adversarial Networks (Demir and Unal, 2018): https://arxiv.org/abs/1803.07422
• Image Segmentation Using DIGITS 5 (Heinrich, 2016): https://developer.nvidia.com/blog/image-segmentation-using-digits-5/
• Stroke of Genius: GauGAN Turns Doodles into Stunning, Photorealistic Landscapes (Salian, 2019): https://blogs.nvidia.com/blog/2019/03/18/gaugan-photorealistic-landscapes-nvidia-research/
• Crowdsourcing the creation of image segmentation algorithms for connectomics (Arganda-Carreras et al., 2015): https://www.frontiersin.org/articles/10.3389/fnana.2015.00142/full
• U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger, Fischer, and Brox, 2015): https://arxiv.org/abs/1505.04597
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (Zhu, Park, Isola, and Efros, 2020): https://arxiv.org/abs/1703.10593
• PyTorch implementation of CycleGAN (2017): https://github.com/togheppi/CycleGAN
• Distribution Matching Losses Can Hallucinate Features in Medical Image Translation (Cohen, Luck, and Honari, 2018): https://arxiv.org/abs/1805.08841
• Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks (Sandfort, Yan, Pickhardt, and Summers, 2019): https://www.nature.com/articles/s41598-019-52737-x.pdf
• Unsupervised Image-to-Image Translation (NVIDIA, 2018): https://github.com/mingyuliutw/UNIT
• Multimodal Unsupervised Image-to-Image Translation (Huang et al., 2018): https://github.com/NVlabs/MUNIT](https://image.slidesharecdn.com/generativeadversarialnetworks-220212125010/85/Generative-adversarial-networks-25-320.jpg)
![25
Papers and Websites
• Semantic Image Synthesis with Spatially-Adaptive Normalization (Park, Liu, Wang, and Zhu, 2019): https://arxiv.org/abs/1903.07291
• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (Ledig et al., 2017): https://arxiv.org/abs/1609.04802
• Multimodal Unsupervised Image-to-Image Translation (Huang et al., 2018): https://github.com/NVlabs/MUNIT
• StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks (Zhang et al., 2017): https://arxiv.org/abs/1612.03242
• Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233
• Snapchat: https://www.snapchat.com
• MaskGAN: Towards Diverse and Interactive Facial Image Manipulation (Lee, Liu, Wu, and Luo, 2020): https://arxiv.org/abs/1907.11922
• When AI generated paintings dance to music... (2019): https://www.youtube.com/watch?v=85l961MmY8Y
• Data Augmentation Generative Adversarial Networks (Antoniou, Storkey, and Edwards, 2018): https://arxiv.org/abs/1711.04340
• Training progression of StyleGAN on H&E tissue fragments (Zhou, 2019): https://twitter.com/realSharonZhou/status/1182877446690852867
• Establishing an evaluation metric to quantify climate change image realism (Sharon Zhou, Luccioni, Cosne, Bernstein, and Bengio, 2020): https://iopscience.iop.org/article/10.1088/2632-2153/ab7657/meta
• Deepfake example (2019): https://en.wikipedia.org/wiki/File:Deepfake_example.gif
• Introduction to adversarial robustness (Kolter and Madry): https://adversarial-ml-tutorial.org/introduction/
• Large Scale GAN Training for High Fidelity Natural Image Synthesis (Brock, Donahue, and Simonyan, 2019): https://openreview.net/pdf?id=B1xsqj09Fm
• GazeGAN - Unpaired Adversarial Image Generation for Gaze Estimation (Sela, Xu, He, Navalpakkam, and Lagun, 2017): https://arxiv.org/abs/1711.09767
• Data Augmentation using GANs for Speech Emotion Recognition (Chatziagapi et al., 2019): https://pdfs.semanticscholar.org/395b/ea6f025e599db710893acb6321e2a1898a1f.pdf
• GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification (Frid-Adar et al., 2018): https://arxiv.org/abs/1803.01229
• GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation (Bowles, Gunn, Hammers, and Rueckert, 2018): https://arxiv.org/abs/1811.10669
• Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks (Sandfort, Yan, Pickhardt, and Summers, 2019): https://www.nature.com/articles/s41598-019-52737-
x/figures/3
• De-identification without losing faces (Li and Lyu, 2019): https://arxiv.org/abs/1902.04202
• Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (Beaulieu-Jones et al., 2019): https://www.ahajournals.org/doi/epub/10.1161/CIRCOUTCOMES.118.005122
• DeepPrivacy: A Generative Adversarial Network for Face Anonymization (Hukkelås, Mester, and Lindseth, 2019): https://arxiv.org/abs/1909.04538
• GAIN: Missing Data Imputation using Generative Adversarial Nets (Yoon, Jordon, and van der Schaar, 2018): https://arxiv.org/abs/1806.02920
• Conditional Infilling GANs for Data Augmentation in Mammogram Classification (E. Wu, K. Wu, Cox, and Lotter, 2018): https://link.springer.com/chapter/10.1007/978-3-030-00946-5_11
• The Effectiveness of Data Augmentation in Image Classification using Deep Learning (Perez and Wang, 2017): https://arxiv.org/abs/1712.04621
• CIFAR-10 and CIFAR-100 Dataset; Learning Multiple Layers of Features from Tiny Images (Krizhevsky, 2009): https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf
• DeOldify... (Antic, 2019): https://twitter.com/citnaj/status/1124904251128406016
• pix2pixHD (Wang et al., 2018): https://github.com/NVIDIA/pix2pixHD
• [4k, 60 fps] Arrival of a Train at La Ciotat (The Lumière Brothers, 1896) (Shiryaev, 2020): https://youtu.be/3RYNThid23g
• Image-to-Image Translation with Conditional Adversarial Networks (Isola, Zhu, Zhou, and Efros, 2018): https://arxiv.org/abs/1611.07004
• Pose Guided Person Image Generation (Ma et al., 2018): https://arxiv.org/abs/1705.09368
• AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks (Xu et al., 2017): https://arxiv.org/abs/1711.10485
• Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233
• Patch-Based Image Inpainting with Generative Adversarial Networks (Demir and Unal, 2018): https://arxiv.org/abs/1803.07422
• Image Segmentation Using DIGITS 5 (Heinrich, 2016): https://developer.nvidia.com/blog/image-segmentation-using-digits-5/
• Stroke of Genius: GauGAN Turns Doodles into Stunning, Photorealistic Landscapes (Salian, 2019): https://blogs.nvidia.com/blog/2019/03/18/gaugan-photorealistic-landscapes-nvidia-research/
• Crowdsourcing the creation of image segmentation algorithms for connectomics (Arganda-Carreras et al., 2015): https://www.frontiersin.org/articles/10.3389/fnana.2015.00142/full
• U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger, Fischer, and Brox, 2015): https://arxiv.org/abs/1505.04597
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (Zhu, Park, Isola, and Efros, 2020): https://arxiv.org/abs/1703.10593
• PyTorch implementation of CycleGAN (2017): https://github.com/togheppi/CycleGAN
• Distribution Matching Losses Can Hallucinate Features in Medical Image Translation (Cohen, Luck, and Honari, 2018): https://arxiv.org/abs/1805.08841
• Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks (Sandfort, Yan, Pickhardt, and Summers, 2019): https://www.nature.com/articles/s41598-019-52737-x.pdf
• Unsupervised Image-to-Image Translation (NVIDIA, 2018): https://github.com/mingyuliutw/UNIT
• Multimodal Unsupervised Image-to-Image Translation (Huang et al., 2018): https://github.com/NVlabs/MUNIT](https://image.slidesharecdn.com/generativeadversarialnetworks-220212125010/75/Generative-adversarial-networks-25-2048.jpg)
Uploaded byDing Li
Generative adversarial networks
Generative Adversarial Networks (GANs) use two neural networks, a generator and discriminator, that compete against each other. The generator learns to generate fake images that look real, while the discriminator learns to tell real images apart from fakes. This document discusses various GAN architectures and applications, including conditional GANs, image-to-image translation, style transfer, semantic image editing, and data augmentation using GAN-generated images. It also covers evaluation metrics for GANs and societal impacts such as bias and deepfakes.
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Generative adversarial networks
- 1. Generative Adversarial Networks(GAN) What is real? Ding Li 2021.4
- 2. Hyper-Realistic Face GeneratorStyleGAN medium
- 3. 3 Generative Adversarial Network Generatorand discriminator learn from the competition with each other. At the end, fakes look real.
- 4.
- 5. 5 Generator: Learning Cost Update to fooldiscriminator better Add randomness to generated picture update to judge real/fake better
- 6. 6 Binary Cross Entropy(BCE) Loss Function
- 7. 7 Example: Generate HandwrittenDigits randn noise 64 Generator block linear Batch Norm ReLU 128 256 512 1024 linear 784 Sigmoid Discriminator block linear Leaky ReLU 512 256 128 1 Ture/False? Generator Discriminator prediction Objective for Prediction Real → 1 Fake → 0 Fake → 1 python ReLU f(z) = max(0, z) Leaky ReLU f(z) = max(0.1z, z) Sigmoid 𝑓(𝑧) = 1 1 + 𝑒−𝑧 28x28
- 8. 8 Covariate Shift &Batch Normalization Covariate Shift Normalization
- 9. 9 Conditional Generation &Interpolation Class vector for digits GAN 0 1 2 3 4 5 6 7 8 9 0 1 0 0 0 0 0 0 0 0 0 0.875 0 0 0 0.125 0 0 0 0 0 0.75 0 0 0 0.25 0 0 0 0 0 0.125 0 0 0 0.875 0 0 0 0 0 0 0 0 0 1 0 0 0 0 …… python
- 10. 10 Controllable Generation &Z-Space Disentanglement python
- 11. 11 Evaluation to pre-train image tocalculate image similarity
- 12. 12 Fréchet Inception Distance(FID) and Inception Score python
- 13.
- 14. PULSE & MachineBias 14 Pulse turns Obama white
- 15. 15 StyleGAN Noise Mapping Style GANStyle GAN 2 python YouTube
- 16. 16 Data Augmentation &Image Synthesis Training with fake data (paper) GauGAN: Semantic Image Synthesis (paper blog YouTube) StackGAN: Text to Photo-realistic Image Synthesis (paper)
- 17. 17 Talking Head Models RealisticNeural Talking Head Models (paper, YouTube)
- 18. 18 Synthesis of DiagnosticQuality Cancer Pathology Images (paper) Sample images (size=1024×1024 pixels) from GANs trained on TCGA Image Dataset (in order: PTC, HCC, LGG, RCC, SCC). Sample images from GANs trained on OVCARE Dataset (in order: CCC, ENC, HGSC, LGSC, MUC) Training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g., rare cancers).
- 19. 19 Image to ImageTranslation pix2pixHD paper GitHub YouTube pix2pix paper GitHub
- 20. 20 Pix2pix Architecture: U-Net+ PatchGAN python
- 21. 21 Cycle GAN: MappingBetween Two Unpaired Piles (paper GitHub YouTube) Cycle Consistency Loss python
- 22. 22 EditGAN: High-Precision SemanticImage Editing (paper GitHub YouTube) (1) EditGAN builds on a GAN framework that jointly models images and their semantic segmentations. (2) (2 & 3) Users can modify segmentation masks, based on which we perform optimization in the GAN’s latent space to realize the edit. (3) (4) Users can perform editing simply by applying previously learnt editing vectors and manipulate images at interactive rates. DatasetGAN for semantic segmentations Raising eyebrows editing. Images are images before editing and after editing. Segmentation masks are before editing and target segmentation mask after manual modification.
- 23.
- 24. 24 Coursera: GenerativeAdversarial Networks (GANs) Specialization Papers and Websites • Generative Adversarial Networks (Goodfellow et al., 2014): https://arxiv.org/abs/1406.2661 • Hyperspherical Variational Auto-Encoders (Davidson, Falorsi, De Cao, Kipf, and Tomczak, 2018): https://www.researchgate.net/figure/Latent-space-visualization-of-the-10-MNIST-digits-in-2-dimensions-of-both-N-VAE- left_fig2_324182043 • Analyzing and Improving the Image Quality of StyleGAN (Karras et al., 2020): https://arxiv.org/abs/1912.04958 • Semantic Image Synthesis with Spatially-Adaptive Normalization (Park, Liu, Wang, and Zhu, 2019): https://arxiv.org/abs/1903.07291 • Few-shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233 • Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (Wu, Zhang, Xue, Freeman, and Tenenbaum, 2017): https://arxiv.org/abs/1610.07584 • These Cats Do Not Exist (Glover and Mott, 2019): http://thesecatsdonotexist.com/ • Large Scale GAN Training for High Fidelity Natural Image Synthesis (Brock, Donahue, and Simonyan, 2019): https://arxiv.org/abs/1809.11096 • Deconvolution and Checkerboard Artifacts (Odena et al., 2016): http://doi.org/10.23915/distill.00003 • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (Radford, Metz, and Chintala, 2016): https://arxiv.org/abs/1511.06434 • Wasserstein GAN (Arjovsky, Chintala, and Bottou, 2017): https://arxiv.org/abs/1701.07875 • Improved Training of Wasserstein GANs (Gulrajani et al., 2017): https://arxiv.org/abs/1704.00028 • Interpreting the Latent Space of GANs for Semantic Face Editing (Shen, Gu, Tang, and Zhou, 2020): https://arxiv.org/abs/1907.10786 • CelebFaces Attributes Dataset (CelebA): http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html • StyleGAN - Official TensorFlow Implementation: https://github.com/NVlabs/stylegan • Stanford Vision Lab: http://vision.stanford.edu/ • Review: Inception-v3 — 1st Runner Up (Image Classification) in ILSVRC 2015 (Tsang, 2018): https://medium.com/@sh.tsang/review-inception-v3-1st-runner-up-image-classification-in-ilsvrc-2015-17915421f77c • HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models (Zhou et al., 2019): https://arxiv.org/abs/1904.01121 • Improved Precision and Recall Metric for Assessing Generative Models (Kynkäänniemi, Karras, Laine, Lehtinen, and Aila, 2019): https://arxiv.org/abs/1904.06991 • Large Scale GAN Training for High Fidelity Natural Image Synthesis (Brock, Donahue, and Simonyan, 2019): https://arxiv.org/abs/1809.11096 • The Fréchet Distance between Multivariate Normal Distributions (Dowson and Landau, 1982): https://core.ac.uk/reader/82269844 • Hyperspherical Variational Auto-Encoders (Davidson, Falorsi, De Cao, Kipf, and Tomczak, 2018): https://arxiv.org/abs/1804.00891 • Generating Diverse High-Fidelity Images with VQ-VAE-2 (Razavi, van den Oord, and Vinyals, 2019): https://arxiv.org/abs/1906.00446 • Conditional Image Generation with PixelCNN Decoders (van den Oord et al., 2016): https://arxiv.org/abs/1606.05328 • Glow: Better Reversible Generative Models (Dhariwal and Kingma, 2018): https://openai.com/blog/glow/ • Machine Bias (Angwin, Larson, Mattu, and Kirchner, 2016): https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing • Fairness Definitions Explained (Verma and Rubin, 2018): https://fairware.cs.umass.edu/papers/Verma.pdf • Does Object Recognition Work for Everyone? (DeVries, Misra, Wang, and van der Maaten, 2019): https://arxiv.org/abs/1906.02659 • PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models (Menon, Damian, Hu, Ravi, and Rudin, 2020): https://arxiv.org/abs/2003.03808 • What a machine learning tool that turns Obama white can (and can't) tell us about AI bias (Vincent, 2020): https://www.theverge.com/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-bias • Mitigating Unwanted Biases with Adversarial Learning (Zhang, Lemoine, and Mitchell, 2018): https://m-mitchell.com/papers/Adversarial_Bias_Mitigation.pdf • Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2020 (Gebru and Denton, 2020): https://sites.google.com/view/fatecv-tutorial/schedule?authuser=0 • Coupled Generative Adversarial Networks (Liu and Tuzel, 2016): https://arxiv.org/abs/1606.07536 • Progressive Growing of GANs for Improved Quality, Stability, and Variation (Karras, Aila, Laine, and Lehtinen, 2018): https://arxiv.org/abs/1710.10196 • A Style-Based Generator Architecture for Generative Adversarial Networks (Karras, Laine, and Aila, 2019): https://arxiv.org/abs/1812.04948 • The Unusual Effectiveness of Averaging in GAN Training (Yazici et al., 2019): https://arxiv.org/abs/1806.04498v2 • Progressive Growing of GANs for Improved Quality, Stability, and Variation (Karras, Aila, Laine, and Lehtinen, 2018): https://arxiv.org/abs/1710.10196 • StyleGAN Faces Training (Branwen, 2019): https://www.gwern.net/images/gan/2019-03-16-stylegan-facestraining.mp4 • Facebook AI Proposes Group Normalization Alternative to Batch Normalization (Peng, 2018): https://medium.com/syncedreview/facebook-ai-proposes-group-normalization-alternative-to-batch-normalization-fb0699bffae7
- 25. 25 Papers andWebsites • Semantic Image Synthesis with Spatially-Adaptive Normalization (Park, Liu, Wang, and Zhu, 2019): https://arxiv.org/abs/1903.07291 • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (Ledig et al., 2017): https://arxiv.org/abs/1609.04802 • Multimodal Unsupervised Image-to-Image Translation (Huang et al., 2018): https://github.com/NVlabs/MUNIT • StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks (Zhang et al., 2017): https://arxiv.org/abs/1612.03242 • Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233 • Snapchat: https://www.snapchat.com • MaskGAN: Towards Diverse and Interactive Facial Image Manipulation (Lee, Liu, Wu, and Luo, 2020): https://arxiv.org/abs/1907.11922 • When AI generated paintings dance to music... (2019): https://www.youtube.com/watch?v=85l961MmY8Y • Data Augmentation Generative Adversarial Networks (Antoniou, Storkey, and Edwards, 2018): https://arxiv.org/abs/1711.04340 • Training progression of StyleGAN on H&E tissue fragments (Zhou, 2019): https://twitter.com/realSharonZhou/status/1182877446690852867 • Establishing an evaluation metric to quantify climate change image realism (Sharon Zhou, Luccioni, Cosne, Bernstein, and Bengio, 2020): https://iopscience.iop.org/article/10.1088/2632-2153/ab7657/meta • Deepfake example (2019): https://en.wikipedia.org/wiki/File:Deepfake_example.gif • Introduction to adversarial robustness (Kolter and Madry): https://adversarial-ml-tutorial.org/introduction/ • Large Scale GAN Training for High Fidelity Natural Image Synthesis (Brock, Donahue, and Simonyan, 2019): https://openreview.net/pdf?id=B1xsqj09Fm • GazeGAN - Unpaired Adversarial Image Generation for Gaze Estimation (Sela, Xu, He, Navalpakkam, and Lagun, 2017): https://arxiv.org/abs/1711.09767 • Data Augmentation using GANs for Speech Emotion Recognition (Chatziagapi et al., 2019): https://pdfs.semanticscholar.org/395b/ea6f025e599db710893acb6321e2a1898a1f.pdf • GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification (Frid-Adar et al., 2018): https://arxiv.org/abs/1803.01229 • GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation (Bowles, Gunn, Hammers, and Rueckert, 2018): https://arxiv.org/abs/1811.10669 • Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks (Sandfort, Yan, Pickhardt, and Summers, 2019): https://www.nature.com/articles/s41598-019-52737- x/figures/3 • De-identification without losing faces (Li and Lyu, 2019): https://arxiv.org/abs/1902.04202 • Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (Beaulieu-Jones et al., 2019): https://www.ahajournals.org/doi/epub/10.1161/CIRCOUTCOMES.118.005122 • DeepPrivacy: A Generative Adversarial Network for Face Anonymization (Hukkelås, Mester, and Lindseth, 2019): https://arxiv.org/abs/1909.04538 • GAIN: Missing Data Imputation using Generative Adversarial Nets (Yoon, Jordon, and van der Schaar, 2018): https://arxiv.org/abs/1806.02920 • Conditional Infilling GANs for Data Augmentation in Mammogram Classification (E. Wu, K. Wu, Cox, and Lotter, 2018): https://link.springer.com/chapter/10.1007/978-3-030-00946-5_11 • The Effectiveness of Data Augmentation in Image Classification using Deep Learning (Perez and Wang, 2017): https://arxiv.org/abs/1712.04621 • CIFAR-10 and CIFAR-100 Dataset; Learning Multiple Layers of Features from Tiny Images (Krizhevsky, 2009): https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf • DeOldify... (Antic, 2019): https://twitter.com/citnaj/status/1124904251128406016 • pix2pixHD (Wang et al., 2018): https://github.com/NVIDIA/pix2pixHD • [4k, 60 fps] Arrival of a Train at La Ciotat (The Lumière Brothers, 1896) (Shiryaev, 2020): https://youtu.be/3RYNThid23g • Image-to-Image Translation with Conditional Adversarial Networks (Isola, Zhu, Zhou, and Efros, 2018): https://arxiv.org/abs/1611.07004 • Pose Guided Person Image Generation (Ma et al., 2018): https://arxiv.org/abs/1705.09368 • AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks (Xu et al., 2017): https://arxiv.org/abs/1711.10485 • Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233 • Patch-Based Image Inpainting with Generative Adversarial Networks (Demir and Unal, 2018): https://arxiv.org/abs/1803.07422 • Image Segmentation Using DIGITS 5 (Heinrich, 2016): https://developer.nvidia.com/blog/image-segmentation-using-digits-5/ • Stroke of Genius: GauGAN Turns Doodles into Stunning, Photorealistic Landscapes (Salian, 2019): https://blogs.nvidia.com/blog/2019/03/18/gaugan-photorealistic-landscapes-nvidia-research/ • Crowdsourcing the creation of image segmentation algorithms for connectomics (Arganda-Carreras et al., 2015): https://www.frontiersin.org/articles/10.3389/fnana.2015.00142/full • U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger, Fischer, and Brox, 2015): https://arxiv.org/abs/1505.04597 • Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (Zhu, Park, Isola, and Efros, 2020): https://arxiv.org/abs/1703.10593 • PyTorch implementation of CycleGAN (2017): https://github.com/togheppi/CycleGAN • Distribution Matching Losses Can Hallucinate Features in Medical Image Translation (Cohen, Luck, and Honari, 2018): https://arxiv.org/abs/1805.08841 • Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks (Sandfort, Yan, Pickhardt, and Summers, 2019): https://www.nature.com/articles/s41598-019-52737-x.pdf • Unsupervised Image-to-Image Translation (NVIDIA, 2018): https://github.com/mingyuliutw/UNIT • Multimodal Unsupervised Image-to-Image Translation (Huang et al., 2018): https://github.com/NVlabs/MUNIT