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Computer graphics has experienced a recent surge of data-centric approaches for photorealistic and controllable content creation.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, and Ross Girshick Kaiming He. 2020 · 2003
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society, 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Generative Adversarial Nets. In Advances in Neural Information Processing Systems (NeurIPS) , Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 2672–2680
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In Proc. of the International Conf. on Learning Representations (ICLR)
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Going deeper with convolutions. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society, 1–9
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015 · 2015
Earlier work this paper cites.
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. 2015 · 2015
Earlier work this paper cites.
Invertible Conditional GANs for image editing
Guim Perarnau, Joost van de Weijer, Bogdan C. Raducanu, and Jose M. Álvarez. 2016 · 2016
Earlier work this paper cites.
Alec Radford, Luke Metz, and Soumith Chintala. 2016 · 2016
Earlier work this paper cites.
Improved Techniques for Training GANs. In Advances in Neural Information Processing Systems (NeurIPS) . 2226–2234
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016 · 2016
Earlier work this paper cites.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems (NeurIPS) . 6626–6637
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017 · 2017
Earlier work this paper cites.
Neural Discrete Representation Learning. In Advances in Neural Information Processing Systems (NeurIPS) , Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 6306–6315
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. 2017 · 2017
Earlier work this paper cites.
Inverting the Generator of a Generative Adversarial Network
Antonia Creswell and Anil Anthony Bharath. 2019 · 2018
Earlier work this paper cites.
The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets. In Proc. IEEE International Conf. on Robotics and Automation (ICRA) . IEEE, 3376–3383
Andreas Fregin, Julian Müller, Ulrich Krebel, and Klaus Dietmayer. 2018 · 2018
Earlier work this paper cites.
Progressive Growing of GANs for Improved Quality, Stability, and Variation. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2018 · 2018
Earlier work this paper cites.
Which Training Methods for GANs do actually Converge?. In Proc. of the International Conf. on Machine learning (ICML) (Proceedings of Machine Learning Research, Vol. 80) . PMLR, 3478–3487
Lars M. Mescheder, Andreas Geiger, and Sebastian Nowozin. 2018 · 2018
Earlier work this paper cites.
Spectral Normalization for Generative Adversarial Networks. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018 · 2018
Earlier work this paper cites.
cGANs with Projection Discriminator. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Takeru Miyato and Masanori Koyama. 2018 · 2018
Earlier work this paper cites.
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . Computer Vision Foundation / IEEE Computer Society, 586–595
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. 2018 · 2018
Earlier work this paper cites.
Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?. In Proc. of the IEEE International Conf. on Computer Vision (ICCV) . 4431–4440
Rameen Abdal, Yipeng Qin, and Peter Wonka. 2019 · 2019
Earlier work this paper cites.
Large Scale GAN Training for High Fidelity Natural Image Synthesis. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019 · 2019
Earlier work this paper cites.
GANalyze: Toward Visual Definitions of Cognitive Image Properties. In Proc. of the IEEE International Conf. on Computer Vision (ICCV) . IEEE, 5743–5752
Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola. 2019 · 2019
Earlier work this paper cites.
A Style-Based Generator Architecture for Generative Adversarial Networks. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . Computer Vision Foundation / IEEE, 4401–4410
Tero Karras, Samuli Laine, and Timo Aila. 2019 · 2019
Earlier work this paper cites.
Improved Precision and Recall Metric for Assessing Generative Models. In Advances in Neural Information Processing Systems (NeurIPS)
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2019 · 2019
Earlier work this paper cites.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proc. of the International Conf. on Machine learning (ICML) (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 6105–6114
Mingxing Tan and Quoc V. Le. 2019 · 2019
Earlier work this paper cites.
TinyGAN: Distilling BigGAN for Conditional Image Generation. In Proc. of the Asian Conf. on Computer Vision (ACCV) (Lecture Notes in Computer Science, Vol. 12625) , Hiroshi Ishikawa, Cheng-Lin Liu, Tomás Pajdla, and Jianbo Shi (Eds.). 509–525
Ting-Yun Chang and Chi-Jen Lu. 2020 · 2020
Cited alongside, same era.
Editing in Style: Uncovering the Local Semantics of GANs. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . Computer Vision Foundation / IEEE, 5770–5779
Edo Collins, Raja Bala, Bob Price, and Sabine Süsstrunk. 2020 · 2020
Cited alongside, same era.
GANSpace: Discovering Interpretable GAN Controls. In Advances in Neural Information Processing Systems (NeurIPS)
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. 2020 · 2020
Cited alongside, same era.
On the "steerability" of generative adversarial networks. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Ali Jahanian, Lucy Chai, and Phillip Isola. 2020 · 2020
Cited alongside, same era.
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal. 2021 · 2021
Later among the works it cites.
Generating images with sparse representations. In Proc. of the International Conf. on Machine learning (ICML)
Charlie Nash, Jacob Menick, Sander Dieleman, and Peter W. Battaglia. 2021 · 2021
Later among the works it cites.
Improved Denoising Diffusion Probabilistic Models. In Proc. of the International Conf. on Machine learning (ICML) (Proceedings of Machine Learning Research, Vol. 139) . PMLR, 8162–8171
Alexander Quinn Nichol and Prafulla Dhariwal. 2021 · 2021
Later among the works it cites.
The Intrinsic Dimension of Images and Its Impact on Learning. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein. 2021 · 2021
Later among the works it cites.
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Training Generative Adversarial Networks with Limited Data. In Advances in Neural Information Processing Systems (NeurIPS)
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2020a · 2020
Cited alongside, same era.
Analyzing and Improving the Image Quality of StyleGAN. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . Computer Vision Foundation / IEEE, 8107–8116
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020b · 2020
Cited alongside, same era.
Reliable Fidelity and Diversity Metrics for Generative Models. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119) . 7176–7185
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo. 2020 · 2020
Cited alongside, same era.
Learning to Detect Objects with a 1 Megapixel Event Camera. In Advances in Neural Information Processing Systems (NeurIPS)
Etienne Perot, Pierre de Tournemire, Davide Nitti, Jonathan Masci, and Amos Sironi. 2020 · 2020
Cited alongside, same era.
InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. 2020 · 2020
Cited alongside, same era.
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. In Advances in Neural Information Processing Systems (NeurIPS)
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng. 2020 · 2020
Cited alongside, same era.
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space. In Proc. of the International Conf. on Machine learning (ICML) . PMLR, 9786–9796
Andrey Voynov and Artem Babenko. 2020 · 2020
Cited alongside, same era.
ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation. In Proc. of the European Conf. on Computer Vision (ECCV) (Lecture Notes in Computer Science, Vol. 12350) . Springer, 365–381
Xucong Zhang, Seonwook Park, Thabo Beeler, Derek Bradley, Siyu Tang, and Otmar Hilliges. 2020 · 2020
Cited alongside, same era.
Do Vision Transformers See Like Convolutional Neural Networks?. In Advances in Neural Information Processing Systems (NeurIPS)
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy. 2021 · 2021
Later among the works it cites.
Pivotal Tuning for Latent-based Editing of Real Images
Daniel Roich, Ron Mokady, Amit H. Bermano, and Daniel Cohen-Or. 2021 · 2021
Later among the works it cites.
Image Super-Resolution via Iterative Refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi. 2021 · 2021
Later among the works it cites.
Projected GANs Converge Faster. In Advances in Neural Information Processing Systems (NeurIPS)
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger. 2021 · 2021
Later among the works it cites.
Counterfactual Generative Networks. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Axel Sauer and Andreas Geiger. 2021 · 2021
Later among the works it cites.
Closed-Form Factorization of Latent Semantics in GANs. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . 1532–1540
Yujun Shen and Bolei Zhou. 2021 · 2021
Later among the works it cites.
Denoising Diffusion Implicit Models. In Proc. of the International Conf. on Learning Representations (ICLR)
Jiaming Song, Chenlin Meng, and Stefano Ermon. 2021 · 2021
Later among the works it cites.
GAN "Steerability" without optimization. In Proc. of the International Conf. on Learning Representations (ICLR) . OpenReview.net
Nurit Spingarn, Ron Banner, and Tomer Michaeli. 2021 · 2021
Later among the works it cites.
Training data-efficient image transformers & distillation through attention. In Proc. of the International Conf. on Machine learning (ICML) (Proceedings of Machine Learning Research, Vol. 139) . PMLR, 10347–10357
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. 2021 · 2021
Later among the works it cites.
Designing an Encoder for StyleGAN Image Manipulation
Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or. 2021 · 2021
Later among the works it cites.
Learning to Efficiently Sample from Diffusion Probabilistic Models
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan. 2021 · 2021
Later among the works it cites.
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . Computer Vision Foundation / IEEE, 12863–12872
Zongze Wu, Dani Lischinski, and Eli Shechtman. 2021 · 2021
Later among the works it cites.
Positional Encoding As Spatial Inductive Bias in GANs. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) . Computer Vision Foundation / IEEE, 13569–13578
Rui Xu, Xintao Wang, Kai Chen, Bolei Zhou, and Chen Change Loy. 2021 · 2021
Later among the works it cites.
Third Time’s the Charm? Image and Video Editing with StyleGAN3
Yuval Alaluf, Or Patashnik, Zongze Wu, Asif Zamir, Eli Shechtman, Dani Lischinski, and Daniel Cohen-Or. 2022 · 2022
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When, Why, and Which Pretrained GANs Are Useful?. In Proc. of the International Conf. on Learning Representations (ICLR)
Timofey Grigoryev, Andrey Voynov, and Artem Babenko. 2022 · 2022
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Making Anime Faces with StyleGAN
Gwern. 2020 · 2022
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Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. 2022 · 2022
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StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation
Umut Kocasari, Alara Dirik, Mert Tiftikci, and Pinar Yanardag. 2022 · 2022
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BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
Daiqing Li, Huan Ling, Seung Wook Kim, Karsten Kreis, Adela Barriuso, Sanja Fidler, and Antonio Torralba. 2022 · 2022
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StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery. In Proc. of the IEEE International Conf. on Computer Vision (ICCV) . 2085–2094
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski. 2021 · 2094
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