papers.json

{
    "papers": [
        {
            "title": "Generative adversarial nets.",
            "authors": [
                "Ian J. Goodfellow",
                "Jean Pouget-Abadie",
                "Mehdi Mirza",
                "Bing Xu",
                "David Warde-Farley",
                "Sherjil Ozair",
                "Aaron Courville",
                "Yoshua Bengio*"
            ],
            "conference": "NeurIPS 2014",
            "links": {
                "pdf": "https://arxiv.org/abs/1406.2661",
                "tutorial": "https://arxiv.org/abs/1701.00160"
            }
        },
        {
            "title": "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.",
            "authors": [
                "Alec Radford",
                "Luke Metz",
                "Soumith Chintala"
            ],
            "conference": "ICLR 2016",
            "links": {
                "pdf": "https://arxiv.org/abs/1511.06434",
                "cited": "11591"
            },
            "model": "DCGAN"
        },
        {
            "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation.",
            "authors": [
                "Tero Karras",
                "Timo Aila",
                "Samuli Laine",
                "Jaakko Lehtinen"
            ],
            "conference": "ICLR 2018",
            "links": {
                "pdf": "https://arxiv.org/abs/1710.10196",
                "cited": "5073"
            },
            "model": "PG-GAN"
        },
        {
            "title": "A Style-Based Generator Architecture for Generative Adversarial Networks.",
            "authors": [
                "Tero Karras",
                "Samuli Laine",
                "Timo Aila"
            ],
            "conference": "CVPR 2019",
            "links": {
                "pdf": "https://arxiv.org/abs/1812.04948",
                "cited": "5514"
            },
            "model": "StyleGAN"
        },
        {
            "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis.",
            "authors": [
                "Andrew Brock",
                "Jeff Donahue",
                "Karen Simonyan"
            ],
            "conference": "ICLR 2019",
            "links": {
                "pdf": "https://arxiv.org/abs/1809.11096",
                "cited": "3604"
            },
            "model": "BigGAN"
        },
        {
            "title": "Analyzing and Improving the Image Quality of StyleGAN.",
            "authors": [
                "Tero Karras",
                "Samuli Laine",
                "Miika Aittala",
                "Janne Hellsten",
                "Jaakko Lehtinen",
                "Timo Aila"
            ],
            "conference": "CVPR 2020",
            "links": {
                "pdf": "https://arxiv.org/abs/1912.04958",
                "cited": "2965"
            },
            "model": "StyleGAN2"
        },
        {
            "title": "Taming Transformers for High-Resolution Image Synthesis",
            "authors": [
                "Patrick Esser",
                "Robin Rombach",
                "Björn Ommer"
            ],
            "conference": "CVPR 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2012.09841",
                "project": "https://compvis.github.io/taming-transformers/",
                "cited": "694"
            },
            "model": "VQGAN"
        },
        {
            "title": "TransGAN: Two Transformers Can Make One Strong GAN, and That Can Scale Up",
            "authors": [
                "Yifan Jiang",
                "Shiyu Chang",
                "Zhangyang Wang"
            ],
            "conference": "CVPR 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2102.07074",
                "pytorch": "https://github.com/asarigun/TransGAN",
                "cited": "157"
            },
            "model": "TransGAN"
        },
        {
            "title": "Alias-Free Generative Adversarial Networks.",
            "authors": [
                "Tero Karras",
                "Miika Aittala",
                "Samuli Laine",
                "Erik Härkönen",
                "Janne Hellsten",
                "Jaakko Lehtinen",
                "Timo Aila"
            ],
            "conference": "NeurIPS 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2106.12423",
                "project": "https://nvlabs.github.io/stylegan3/",
                "cited": "568"
            },
            "model": "StyleGAN3"
        },
        {
            "title": "StyleSwin: Transformer-based GAN for High-resolution Image Generation",
            "authors": [
                "Bowen Zhang",
                "Shuyang Gu",
                "Bo Zhang",
                "Jianmin Bao",
                "Dong Chen",
                "Fang Wen",
                "Yong Wang",
                "Baining Guo"
            ],
            "conference": "CVPR 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2112.10762",
                "cited": "49"
            },
            "model": "StyleSwin"
        },
        {
            "title": "Scaling StyleGAN to Large Diverse Datasets",
            "authors": [
                "Axel Sauer",
                "Katja Schwarz",
                "Andreas Geiger"
            ],
            "conference": "SIGGRAPH 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2202.00273",
                "cited": "118"
            },
            "model": "StyleGAN-XL"
        },
        {
            "title": "A Large-Scale Study on Regularization and Normalization in GANs",
            "authors": [
                "Karol Kurach",
                "Mario Lucic",
                "Xiaohua Zhai",
                "Marcin Michalski",
                "Sylvain Gelly"
            ],
            "conference": "ICML 2019",
            "links": {
                "pdf": "https://arxiv.org/abs/1807.04720",
                "cited": "141"
            }
        },
        {
            "title": "Energy-based Generative Adversarial Networks",
            "authors": [
                "Junbo Zhao",
                "Michael Mathieu",
                "Yann LeCun"
            ],
            "conference": "ICLR 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1609.03126",
                "cited": "978"
            },
            "model": "EB-GAN"
        },
        {
            "title": "Towards Principled Methods for Training Generative Adversarial Networks",
            "authors": [
                "Martin Arjovsky",
                "Léon Bottou"
            ],
            "conference": "ICLR 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1701.04862",
                "cited": "1696"
            }
        },
        {
            "title": "Least Squares Generative Adversarial Networks",
            "authors": [
                "Xudong Mao",
                "Qing Li",
                "Haoran Xie",
                "Raymond Y.K. Lau",
                "Zhen Wang",
                "Stephen Paul Smolley"
            ],
            "conference": "ICCV 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1611.04076",
                "cited": "3504"
            },
            "model": "LSGAN"
        },
        {
            "title": "Wasserstein GAN",
            "authors": [
                "Martin Arjovsky",
                "Soumith Chintala",
                "Léon Bottou"
            ],
            "conference": "ICML 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1701.07875",
                "cited": "3159"
            },
            "model": "WGAN"
        },
        {
            "title": "Geometric GAN",
            "authors": [
                "Jae Hyun Lim",
                "Jong Chul Ye"
            ],
            "conference": "Axiv 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1705.02894",
                "cited": "252"
            },
            "model": "GGAN"
        },
        {
            "title": "Conditional Image Synthesis With Auxiliary Classifier GANs",
            "authors": [
                "Augustus Odena",
                "Christopher Olah",
                "Jonathon Shlens"
            ],
            "conference": "ICML 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1610.09585",
                "cited": "2550"
            },
            "model": "AC-GAN"
        },
        {
            "title": "cGANs with Projection Discriminator",
            "authors": [
                "Takeru Miyato",
                "Masanori Koyama"
            ],
            "conference": "ICLR 2018",
            "links": {
                "pdf": "https://arxiv.org/abs/1802.05637",
                "cited": "636"
            }
        },
        {
            "title": "High-Fidelity Image Generation With Fewer Labels",
            "authors": [
                "Mario Lucic*",
                "Michael Tschannen*",
                "Marvin Ritter*",
                "Xiaohua Zhai",
                "Olivier Bachem",
                "Sylvain Gelly"
            ],
            "conference": "ICML 2019",
            "links": {
                "pdf": "https://arxiv.org/abs/1903.02271",
                "project": "https://github.com/google/compare_gan",
                "cited": "123"
            },
            "model": "S³-GAN"
        },
        {
            "title": "Variational Inference with Normalizing Flows",
            "authors": [
                "Danilo Jimenez Rezende",
                "Shakir Mohamed"
            ],
            "conference": "ICML 2015",
            "links": {
                "pdf": "https://arxiv.org/abs/1505.05770",
                "cited": "2623"
            }
        },
        {
            "title": "Improved Variational Inference with Inverse Autoregressive Flow",
            "authors": [
                "Diederik P. Kingma",
                "Tim Salimans",
                "Rafal Jozefowicz",
                "Xi Chen",
                "Ilya Sutskever",
                "Max Welling"
            ],
            "conference": "NeurIPS 2016",
            "links": {
                "pdf": "https://arxiv.org/abs/1606.04934",
                "cited": "1449"
            }
        },
        {
            "title": "NVAE: A Deep Hierarchical Variational Autoencoder",
            "authors": [
                "Arash Vahdat",
                "Jan Kautz"
            ],
            "conference": "NeurIPS 2020",
            "links": {
                "pdf": "https://arxiv.org/abs/2007.03898",
                "cited": "439"
            }
        },
        {
            "title": "Improved techniques for training score-based generative models.",
            "authors": [
                "Yang Song",
                "Stefano Ermon"
            ],
            "conference": "NeurIPS 2020",
            "links": {
                "pdf": "https://arxiv.org/abs/2006.09011",
                "cited": "365"
            }
        },
        {
            "title": "Denoising Diffusion Probabilistic Models",
            "authors": [
                "Jonathan Ho",
                "Ajay Jain",
                "Pieter Abbeel"
            ],
            "conference": "NeurIPS 2020",
            "links": {
                "pdf": "https://arxiv.org/abs/2006.11239",
                "cited": "1852"
            },
            "model": "DDPM"
        },
        {
            "title": "Score-based generative modeling through stochastic differential equations",
            "authors": [
                "Yang Song",
                "Jascha Sohl-Dickstein",
                "Diederik P Kingma",
                "Abhishek Kumar",
                "Stefano Ermon",
                "Ben Poole"
            ],
            "conference": "ICLR 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2011.13456",
                "cited": "915"
            }
        },
        {
            "title": "Improved Denoising Diffusion Probabilistic Models",
            "authors": [
                "Alex Nichol",
                "Prafulla Dhariwal"
            ],
            "conference": "ICML 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2102.09672",
                "cited": "631"
            },
            "model": "Improved-DDPM"
        },
        {
            "title": "Variational Diffusion Models.",
            "authors": [
                "Diederik P. Kingma",
                "Tim Salimans",
                "Ben Poole",
                "Jonathan Ho"
            ],
            "conference": "NeurIPS 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2107.00630",
                "cited": "244"
            }
        },
        {
            "title": "Diffusion Models Beat GANs on Image Synthesis",
            "authors": [
                "Prafulla Dhariwal",
                "Alex Nichol"
            ],
            "conference": "NeurIPS 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2105.05233",
                "cited": "1094"
            },
            "model": "Guided-Diffusion"
        },
        {
            "title": "Classifier-Free Diffusion Guidance.",
            "authors": [
                "Jonathan Ho",
                "Tim Salimans"
            ],
            "conference": "NeurIPS 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2207.12598",
                "cited": "399"
            }
        },
        {
            "title": "SDEdit: Image Synthesis and Editing with Stochastic Differential Equations",
            "authors": [
                "Chenlin Meng",
                "Yutong He",
                "Yang Song",
                "Jiaming Song",
                "Jiajun Wu",
                "Jun-Yan Zhu",
                "Stefano Ermon"
            ],
            "conference": "ICLR 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2108.01073",
                "cited": "152"
            }
        },
        {
            "title": "DiffusionCLIP: Text-guided Image Manipulation Using Diffusion Models",
            "authors": [
                "Gwanghyun Kim",
                "Taesung Kwon",
                "Jong Chul Ye"
            ],
            "conference": "CVPR 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2110.02711",
                "cited": "101"
            }
        },
        {
            "title": "Blended Diffusion: Text-driven Editing of Natural Images",
            "authors": [
                "Omri Avrahami",
                "Dani Lischinski",
                "Ohad Fried"
            ],
            "conference": "CVPR 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2111.14818",
                "cited": "148"
            }
        },
        {
            "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
            "authors": [
                "Alex Nichol",
                "Prafulla Dhariwal",
                "Aditya Ramesh",
                "Pranav Shyam",
                "Pamela Mishkin",
                "Bob McGrew",
                "Ilya Sutskever",
                "Mark Chen"
            ],
            "conference": "ICML 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2112.10741",
                "cited": "615"
            }
        },
        {
            "title": "Palette: Image-to-Image diffusion models.",
            "authors": [
                "Chitwan Saharia",
                "William Chan",
                "Huiwen Chang",
                "Chris A. Lee",
                "Jonathan Ho",
                "Tim Salimans",
                "David J. Fleet",
                "Mohammad Norouzi"
            ],
            "conference": "SIGGRAPH 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2111.05826",
                "cited": "274"
            }
        },
        {
            "title": "RePaint: Inpainting using Denoising Diffusion Probabilistic Models",
            "authors": [
                "Andreas Lugmayr",
                "Martin Danelljan",
                "Andres Romero",
                "Fisher Yu",
                "Radu Timofte",
                "Luc Van Gool"
            ],
            "conference": "CVPR 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2201.09865",
                "cited": "194"
            }
        },
        {
            "title": "Deep Convolutional Inverse Graphics Network",
            "authors": [
                "Tejas D. Kulkarni",
                "Will Whitney",
                "Pushmeet Kohli",
                "Joshua B. Tenenbaum"
            ],
            "conference": "NeurIPS 2015",
            "links": {
                "pdf": "Deep Convolutional Inverse Graphics Network"
            },
            "model": "DC-IGN"
        },
        {
            "title": "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets",
            "authors": [
                "Xi Chen",
                "Yan Duan",
                "Rein Houthooft",
                "John Schulman",
                "Ilya Sutskever",
                "Pieter Abbeel"
            ],
            "conference": "NeurIPS 2016",
            "links": {
                "pdf": "https://arxiv.org/abs/1606.03657",
                "cited": "3573"
            },
            "model": "InfoGAN"
        },
        {
            "title": "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework",
            "authors": [
                "I. Higgins",
                "L. Matthey",
                "Arka Pal",
                "Christopher P. Burgess",
                "Xavier Glorot",
                "M. Botvinick",
                "S. Mohamed",
                "Alexander Lerchner"
            ],
            "conference": "ICLR 2017",
            "links": {
                "pdf": "https://openreview.net/forum?id=Sy2fzU9gl"
            },
            "model": "Beta-VAE"
        },
        {
            "title": "Understanding disentangling in β-VAE",
            "authors": [
                "Christopher P. Burgess",
                "Irina Higgins",
                "Arka Pal",
                "Loic Matthey",
                "Nick Watters",
                "Guillaume Desjardins",
                "Alexander Lerchner"
            ],
            "conference": "NeurIPS 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1804.03599",
                "cited": "318"
            },
            "model": "AnnealedVAE"
        },
        {
            "title": "Disentangling by Factorising",
            "authors": [
                "Hyunjik Kim",
                "Andriy Mnih"
            ],
            "conference": "NeurIPS 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1802.05983",
                "cited": "986"
            },
            "model": "Factor-VAE"
        },
        {
            "title": "A framework for the quantitative evaluation of disentangled representations.",
            "authors": [
                "Cian Eastwood",
                "Christopher K. I. Williams"
            ],
            "conference": "ICLR 2018",
            "links": {
                "pdf": "https://openreview.net/pdf?id=By-7dz-AZ"
            },
            "model": "DCI"
        },
        {
            "title": "Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.",
            "authors": [
                "Francesco Locatello",
                "Stefan Bauer",
                "Mario Lucic",
                "Gunnar Rätsch",
                "Sylvain Gelly",
                "Bernhard Schölkopf",
                "Olivier Bachem"
            ],
            "conference": "ICML(best paper award) 2019",
            "links": {
                "pdf": "https://arxiv.org/abs/1811.12359",
                "cited": "974"
            }
        },
        {
            "title": "Improved training of wasserstein gans",
            "authors": [
                "Ishaan Gulrajani",
                "Faruk Ahmed",
                "Martin Arjovsky",
                "Vincent Dumoulin",
                "Aaron Courville"
            ],
            "conference": "NeurIPS 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1704.00028",
                "cited": "7006"
            },
            "model": "WGAN-GP"
        },
        {
            "title": "The Numerics of GANs",
            "authors": [
                "Lars Mescheder",
                "Sebastian Nowozin",
                "Andreas Geiger"
            ],
            "conference": "NeurIPS 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1705.10461",
                "cited": "390"
            }
        },
        {
            "title": "Which Training Methods for GANs do actually Converge?",
            "authors": [
                "Lars Mescheder",
                "Andreas Geiger",
                "Sebastian Nowozin"
            ],
            "conference": "ICML 2018",
            "links": {
                "pdf": "https://arxiv.org/abs/1801.04406",
                "cited": "1085"
            },
            "model": "R1-regularization"
        },
        {
            "title": "Spectral Normalization for Generative Adversarial Networks.",
            "authors": [
                "Takeru Miyato",
                "Toshiki Kataoka",
                "Masanori Koyama",
                "Yuichi Yoshida"
            ],
            "conference": "ICLR 2018",
            "links": {
                "pdf": "https://arxiv.org/abs/1802.05957",
                "cited": "3378"
            },
            "model": "SN-GAN"
        },
        {
            "title": "Consistency regularization for generative adversarial networks.",
            "authors": [
                "Han Zhang",
                "Zizhao Zhang",
                "Augustus Odena",
                "Honglak Lee"
            ],
            "conference": "ICLR 2020",
            "links": {
                "pdf": "https://arxiv.org/abs/1910.12027",
                "cited": "192"
            },
            "model": "CR-GAN"
        },
        {
            "title": "Differentiable Augmentation for Data-Efficient GAN Training",
            "authors": [
                "Zhao Shengyu",
                "Liu Zhijian",
                "Lin Ji",
                "Zhu Jun-Yan",
                "Han Song"
            ],
            "conference": "NeurIPS 2020",
            "links": {
                "pdf": "https://arxiv.org/abs/2006.10738",
                "project": "https://hanlab.mit.edu/projects/data-efficient-gans/"
            },
            "cited": "316"
        },
        {
            "title": "Improved consistency regularization for GANs",
            "authors": [
                "Zhengli Zhao",
                "Sameer Singh",
                "Honglak Lee",
                "Zizhao Zhang",
                "Augustus Odena",
                "Han Zhang"
            ],
            "conference": "AAAI 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2002.04724"
            },
            "cited": "86"
        },
        {
            "title": "Training Generative Adversarial Networks with Limited Data",
            "authors": [
                "Tero Karras",
                "Miika Aittala",
                "Janne Hellsten",
                "Samuli Laine",
                "Jaakko Lehtinen",
                "Timo Aila"
            ],
            "conference": "NeurIPS 2020",
            "links": {
                "pdf": "https://arxiv.org/abs/2006.06676",
                "tensorflow": "https://github.com/NVlabs/stylegan2-ada",
                "pytorch": "https://github.com/NVlabs/stylegan2-ada-pytorch"
            },
            "cited": "921"
        },
        {
            "title": "Gradient Normalization for Generative Adversarial Networks",
            "authors": [
                "Yi-Lun Wu",
                "Hong-Han Shuai",
                "Zhi-Rui Tam",
                "Hong-Yu Chiu"
            ],
            "conference": "ICCV 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2109.02235"
            },
            "cited": "23"
        },
        {
            "title": "Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data",
            "authors": [
                "Liming Jiang",
                "Bo Dai",
                "Wayne Wu",
                "Chen Change Loy"
            ],
            "conference": "NeurIPS 2021",
            "links": {
                "pdf": "https://arxiv.org/abs/2111.06849"
            },
            "cited": "35"
        },
        {
            "title": "Improved Techniques for Training GANs",
            "authors": [
                "Tim Salimans",
                "Ian Goodfellow",
                "Wojciech Zaremba",
                "Vicki Cheung",
                "Alec Radford",
                "Xi Chen"
            ],
            "conference": "NeurIPS 2016",
            "links": {
                "pdf": "https://arxiv.org/abs/1606.03498"
            },
            "metric": "Inception-Score/IS",
            "cited": "6678"
        },
        {
            "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
            "authors": [
                "Martin Heusel",
                "Hubert Ramsauer",
                "Thomas Unterthiner",
                "Bernhard Nessler",
                "Sepp Hochreiter"
            ],
            "conference": "NeurIPS 2017",
            "links": {
                "pdf": "https://arxiv.org/abs/1706.08500"
            },
            "metric": "FID, TTUR",
            "cited": "6472"
        },
        {
            "title": "Sliced Wasserstein Generative Models",
            "authors": [
                "Jiqing Wu",
                "Zhiwu Huang",
                "Dinesh Acharya",
                "Wen Li",
                "Janine Thoma",
                "Danda Pani Paudel",
                "Luc Van Gool"
            ],
            "conference": "CVPR 2019",
            "links": {
                "pdf": "https://arxiv.org/abs/1706.02631"
            },
            "metric": "SWD",
            "cited": "0"
        },
        {
            "title": "Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis",
            "authors": [
                "Bingchen Liu",
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            "title": "Image Generation From Small Datasets via Batch Statistics Adaptation",
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            "title": "Minegan: effective knowledge transfer from gans to target domains with few images",
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            "title": "One-Shot Domain Adaptation For Face Generation",
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                "Chao Yang",
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                "Jing Liu",
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            "title": "Few-shot Image Generation via Cross-domain Correspondence",
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                "Jingwan Lu",
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            "title": "StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators",
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                "Ceyuan Yang",
                "Yujun Shen*",
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                "Yinghao Xu",
                "Jiapeng Zhu",
                "Zhirong Wu",
                "Bolei Zhou"
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                "pdf": "https://arxiv.org/pdf/2106.06627.pdf"
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                "Jiayu Xiao",
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                "Qingming Huang"
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                "David Forsyth"
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                "Ran Yi",
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                "Michaël Gharbi",
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        {
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            "title": "Masked Generative Image Transformer",
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                "Lu Jiang",
                "Ce Liu",
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                "pdf": "https://arxiv.org/abs/2202.04200"
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        {
            "title": "VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance",
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                "pdf": "https://arxiv.org/abs/2204.08583"
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        {
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                "Difan Liu",
                "Sandesh Shetty",
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                "Matthew Fisher",
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            "title": "CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP",
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                "Zihao Wang",
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                "pdf": "https://arxiv.org/abs/2203.00386"
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        {
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            "title": "High-Quality Pluralistic Image Completion via Code Shared VQGAN",
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                "Guoxian Song",
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                "pdf": "https://arxiv.org/abs/2204.01931"
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        {
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        {
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        {
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                "Huiwen Chang",
                "Han Zhang",
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                "Ce Liu",
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            "conference": "arxiv 2022",
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        {
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        {
            "title": "Vector Quantized Diffusion Model for Text-to-Image Synthesis",
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                "Shuyang Gu",
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        {
            "title": "Improved Vector Quantized Diffusion Models",
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                "pdf": "https://arxiv.org/abs/2205.16007"
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            "cited": "24"
        },
        {
            "title": "Text2Human: Text-Driven Controllable Human Image Generation",
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                "Yuming Jiang",
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                "Chen Change Loy",
                "Ziwei Liu"
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                "pdf": "https://arxiv.org/abs/2205.15996"
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        },
        {
            "title": "Autoregressive image generation using residual quantization",
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                "Doyup Lee",
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                "pdf": "https://arxiv.org/abs/2203.01941"
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        },
        {
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        {
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            "links": {
                "pdf": "link to pdf"
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            "links": {
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                "Yi Yang"
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                "Xiaoyuan Jing",
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                "Alexey Gritsenko",
                "William Chan",
                "Mohammad Norouzi",
                "David J. Fleet"
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                "pdf": "https://arxiv.org/abs/2204.03458"
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            "cited": "183",
            "model": null
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        {
            "title": "Make-a-video: Text-to-video generation without text-video data",
            "authors": [
                "Uriel Singer",
                "Adam Polyak",
                "Thomas Hayes",
                "Xi Yin",
                "Jie An",
                "Songyang Zhang",
                "Qiyuan Hu",
                "Harry Yang",
                "Oron Ashual",
                "Oran Gafni",
                "Devi Parikh",
                "Sonal Gupta",
                "Yaniv Taigman"
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            "conference": "arxiv 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2209.14792"
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            "cited": "134",
            "model": null
        },
        {
            "title": "Tune-A-Video: Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation",
            "authors": [
                "Jay Zhangjie Wu",
                "Yixiao Ge",
                "Xintao Wang",
                "Weixian Lei",
                "Yuchao Gu",
                "Wynne Hsu",
                "Ying Shan",
                "Xiaohu Qie",
                "Mike Zheng Shou"
            ],
            "conference": "arxiv 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2212.11565"
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            "cited": "22",
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        {
            "title": "Deep Image Prior",
            "authors": [
                "Dmitry Ulyanov",
                "Andrea Vedaldi",
                "Victor Lempitsky"
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            "links": {
                "pdf": "https://arxiv.org/abs/1711.10925",
                "project": "https://dmitryulyanov.github.io/deep_image_prior"
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            "cited": "1947",
            "model": null
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        {
            "title": "SinGAN: Learning a Generative Model from a Single Natural Image",
            "authors": [
                "Tamar Rott Shaham",
                "Tali Dekel",
                "Tomer Michaeli"
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                "pdf": "https://arxiv.org/abs/1905.01164",
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            "cited": "543",
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        {
            "title": "TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images",
            "authors": [
                "Jianxin Lin",
                "Yingxue Pang",
                "Yingce Xia",
                "Zhibo Chen",
                "Jiebo Luo"
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                "pdf": "https://arxiv.org/abs/2004.04634"
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            "cited": "38",
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        {
            "title": "Image Shape Manipulation from a Single Augmented Training Sample",
            "authors": [
                "Yael Vinker",
                "Eliahu Horwitz",
                "Nir Zabari",
                "Yedid Hoshen"
            ],
            "conference": "ICCV 2021",
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                "pdf": "https://arxiv.org/abs/2007.01289",
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            "model": "DeepSIM"
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        {
            "title": "Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization",
            "authors": [
                "Daiqing Li",
                "Junlin Yang",
                "Karsten Kreis",
                "Antonio Torralba",
                "Sanja Fidler"
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            "links": {
                "pdf": "https://arxiv.org/abs/2104.05833"
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        {
            "title": "DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort",
            "authors": [
                "Yuxuan Zhang",
                "Huan Ling",
                "Jun Gao",
                "Kangxue Yin",
                "Jean-Francois Lafleche",
                "Adela Barriuso",
                "Antonio Torralba",
                "Sanja Fidler"
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                "pdf": "https://arxiv.org/abs/2104.06490"
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        {
            "title": "SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing",
            "authors": [
                "Yichun Shi",
                "Xiao Yang",
                "Yangyue Wan",
                "Xiaohui Shen"
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                "pdf": "https://arxiv.org/abs/2112.02236"
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        {
            "title": "Learning to generate line drawings that convey geometry and semantics",
            "authors": [
                "Caroline Chan",
                "Fredo Durand",
                "Phillip Isola"
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            "conference": "arxiv 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2203.12691"
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            "model": null
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        {
            "title": "Synthesizing the preferred inputs for neurons in neural networks via deep generator networks.",
            "authors": [
                "Anh Nguyen",
                "Alexey Dosovitskiy",
                "Jason Yosinski",
                "Thomas Brox",
                "Jeff Clune"
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            "links": {
                "pdf": "https://arxiv.org/abs/1605.09304"
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        {
            "title": "Generating Images with Perceptual Similarity Metrics based on Deep Networks.",
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                "Thomas Brox"
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                "pdf": "https://arxiv.org/abs/1602.02644"
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        {
            "title": "VectorFusion Text-to-SVG by Abstracting Pixel-Based Diffusion Models",
            "authors": [
                "Ajay Jain",
                "Amber Xie",
                "Pieter Abbeel"
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            "conference": "arxiv 2022",
            "links": {
                "pdf": "https://arxiv.org/abs/2211.11319"
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    ]
}