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This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration.
Guocheng Qian, Jinjin Gu, Jimmy Ren, Chao Dong, Furong Zhao, and Juan Lin. 2019 · 1905
Earlier work this paper cites.
Guided Image Generation with Conditional Invertible Neural Networks. In arXiv:1907.02392
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and Ullrich Köthe. 2019 · 1907
Earlier work this paper cites.
Image inpainting. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques . 417–424
Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. 2000 · 2000
Earlier work this paper cites.
A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In ICCV
D. Martin, C. Fowlkes, D. Tal, and J. Malik. 2001 · 2001
Earlier work this paper cites.
Scene completion using millions of photographs
James Hays and Alexei A Efros. 2007 · 2007
Earlier work this paper cites.
PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing
Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman. 2009 · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition . Ieee, 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Statistics of patch offsets for image completion. In European conference on computer vision . Springer, 16–29
Kaiming He and Jian Sun. 2012 · 2012
Earlier work this paper cites.
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
Soo Ye Kim, Kfir Aberman, Nori Kanazawa, Rahul Garg, Neal Wadhwa, Huiwen Chang, Nikhil Karnad, Munchurl Kim, and Orly Liba. 2021a · 2012
Earlier work this paper cites.
Quality prediction for image completion
Johannes Kopf, Wolf Kienzle, Steven Drucker, and Sing Bing Kang. 2012 · 2012
Earlier work this paper cites.
Auto-Encoding Variational Bayes. In ICLR
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Generative Adversarial Networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Photo uncrop. In European Conference on Computer Vision . Springer, 16–31
Qi Shan, Brian Curless, Yasutaka Furukawa, Carlos Hernandez, and Steven M Seitz. 2014 · 2014
Earlier work this paper cites.
Biggerpicture: data-driven image extrapolation using graph matching
Miao Wang, Yu-Kun Lai, Yuan Liang, Ralph R Martin, and Shi-Min Hu. 2014 · 2014
Earlier work this paper cites.
Compression artifacts reduction by a deep convolutional network. In Proceedings of the IEEE International Conference on Computer Vision . 576–584
Chao Dong, Yubin Deng, Chen Change Loy, and Xiaoou Tang. 2015 · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics. In ICML . PMLR, 2256–2265
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2016 · 2016
Earlier work this paper cites.
Generating Images with Perceptual Similarity Metrics based on Deep Networks
Alexey Dosovitskiy and Thomas Brox. 2016 · 2016
Earlier work this paper cites.
Deeply-recursive convolutional network for image super-resolution. In CVPR . 1637–1645
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016 · 2016
Earlier work this paper cites.
Learning representations for automatic colorization. In European conference on computer vision . Springer, 577–593
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. 2016 · 2016
Earlier work this paper cites.
Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein. 2016 · 2016
Earlier work this paper cites.
Unsupervised cross-domain image generation
Yaniv Taigman, Adam Polyak, and Lior Wolf. 2016 · 2016
Earlier work this paper cites.
Conditional image generation with PixelCNN decoders. In NIPS . 4790–4798
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. 2016 · 2016
Earlier work this paper cites.
Colorful image colorization. In European conference on computer vision . Springer, 649–666
Richard Zhang, Phillip Isola, and Alexei A Efros. 2016 · 2016
Earlier work this paper cites.
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. In CVPRW
Eirikur Agustsson and Radu Timofte. 2017 · 2017
Earlier work this paper cites.
Wasserstein GAN. In arXiv
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017 · 2017
Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. 2017 · 2017
Earlier work this paper cites.
Pixel recursive super resolution. In ICCV
Ryan Dahl, Mohammad Norouzi, and Jonathon Shlens. 2017 · 2017
Earlier work this paper cites.
Learning diverse image colorization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 6837–6845
Aditya Deshpande, Jiajun Lu, Mao-Chuang Yeh, Min Jin Chong, and David Forsyth. 2017 · 2017
Cited alongside, same era.
Deep generative adversarial compression artifact removal. In Proceedings of the IEEE International Conference on Computer Vision . 4826–4835
Leonardo Galteri, Lorenzo Seidenari, Marco Bertini, and Alberto Del Bimbo. 2017 · 2017
Cited alongside, same era.
Pixcolor: Pixel recursive colorization
Sergio Guadarrama, Ryan Dahl, David Bieber, Mohammad Norouzi, Jonathon Shlens, and Kevin Murphy. 2017 · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017 · 2017
Cited alongside, same era.
Globally and locally consistent image completion
PULSE: Self-supervised photo upsampling via latent space exploration of generative models. In CVPR
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin. 2020 · 2020
Later among the works it cites.
Improved Techniques for Training Score-Based Generative Models
Yang Song and Stefano Ermon. 2020 · 2020
Later among the works it cites.
Instance-aware image colorization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 7968–7977
Jheng-Wei Su, Hung-Kuo Chu, and Jia-Bin Huang. 2020 · 2020
Later among the works it cites.
NVAE: A Deep Hierarchical Variational Autoencoder. In NeurIPS
Arash Vahdat and Jan Kautz. 2020 · 2020
Later among the works it cites.
Contextual residual aggregation for ultra high-resolution image inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 7508–7517
Zili Yi, Qiang Tang, Shekoofeh Azizi, Daesik Jang, and Zhan Xu. 2020 · 2020
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Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. 2017 · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network. In ICCV
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Cited alongside, same era.
Probabilistic Image Colorization. In arXiv:1705.04258
Amelie Royer, Alexander Kolesnikov, and Christoph H. Lampert. 2017 · 2017
Cited alongside, same era.
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. In ICLR
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P. Kingma. 2017 · 2017
Cited alongside, same era.
Attention Is All You Need. In NIPS
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Places: A 10 million Image Database for Scene Recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017 · 2017
Cited alongside, same era.
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition . 8789–8797
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2018 · 2018
Cited alongside, same era.
Glow: Generative Flow with Invertible 1x1 Convolutions. In NIPS
Diederik P. Kingma and Prafulla Dhariwal. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Uctgan: Diverse image inpainting based on unsupervised cross-space translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5741–5750
Lei Zhao, Qihang Mo, Sihuan Lin, Zhizhong Wang, Zhiwen Zuo, Haibo Chen, Wei Xing, and Dongming Lu. 2020 · 2020
Later among the works it cites.
Structured Denoising Diffusion Models in Discrete State-Spaces
Jacob Austin, Daniel Johnson, Jonathan Ho, Danny Tarlow, and Rianne van den Berg. 2021 · 2021
Closest in time.
OCONet: Image Extrapolation by Object Completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2307–2317
Richard Strong Bowen, Huiwen Chang, Charles Herrmann, Piotr Teterwak, Ce Liu, and Ramin Zabih. 2021 · 2021
Closest in time.
In&Out: Diverse Image Outpainting via GAN Inversion
Yen-Chi Cheng, Chieh Hubert Lin, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, and Ming-Hsuan Yang. 2021 · 2021
Closest in time.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alex Nichol. 2021 · 2021
Closest in time.
Cascaded Diffusion Models for High Fidelity Image Generation. In arXiv
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. 2021 · 2021
Closest in time.
Argmax flows and multinomial diffusion: Towards non-autoregressive language models
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling. 2021 · 2021
Closest in time.
Gotta Go Fast When Generating Data with Score-Based Models. In arXiv preprint arXiv:2105.14080
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, and Ioannis Mitliagkas. 2021 · 2021
Closest in time.
Solving linear inverse problems using the prior implicit in a denoiser
Zahra Kadkhodaie and Eero P Simoncelli. 2021 · 2021
Closest in time.
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho. 2021 · 2021
Closest in time.
DiffWave: A Versatile Diffusion Model for Audio Synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. 2021 · 2021
Closest in time.
Colorization Transformer. In ICLR 2021
Manoj Kumar, Dirk Weissenborn, and Nal Kalchbrenner. 2021 · 2021
Closest in time.
InfinityGAN: Towards Infinite-Resolution Image Synthesis
Chieh Hubert Lin, Hsin-Ying Lee, Yen-Chi Cheng, Sergey Tulyakov, and Ming-Hsuan Yang. 2021 · 2021
Closest in time.
SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. 2021 · 2021
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Improved Denoising Diffusion Probabilistic Models
Alex Nichol and Prafulla Dhariwal. 2021 · 2021
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Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. 2021 · 2021
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UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models
Hiroshi Sasaki, Chris G Willcocks, and Toby P Breckon. 2021 · 2021
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D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Abhishek Sinha, Jiaming Song, Chenlin Meng, and Stefano Ermon. 2021 · 2021
Closest in time.
Score-Based Generative Modeling through Stochastic Differential Equations. In ICLR
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021 · 2021
Closest in time.
Score-based Generative Modeling in Latent Space
Arash Vahdat, Karsten Kreis, and Jan Kautz. 2021 · 2021
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Scaling local self-attention for parameter efficient visual backbones. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 12894–12904
Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, and Jonathon Shlens. 2021 · 2021
Closest in time.
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan. 2021 · 2021
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Large scale image completion via co-modulated generative adversarial networks
Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I Chang, and Yan Xu. 2021 · 2021
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