Fetching the paper…
Reading the bibliography…
Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks.
Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 1904
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Large-scale classification of fine-art paintings: Learning the right metric on the right feature
Babak Saleh and Ahmed M. Elgammal · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
Earlier work this paper cites.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus), 2016
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, I. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
Earlier work this paper cites.
The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Earlier work this paper cites.
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
Earlier work this paper cites.
Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Earlier work this paper cites.
Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper R. R. Uijlings, and Vittorio Ferrari · 2018
Cited alongside, same era.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann N. Dauphin · 2018
Cited alongside, same era.
Sex, lies, and videotape: Deep fakes and free speech delusions
Mary Anne Franks and Ari Ezra Waldman · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper R. R. Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, and Vittorio Ferrari · 2018
Cited alongside, same era.
Ethical considerations of generative ai
Emily Denton · 2021
Later among the works it cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Later among the works it cites.
Vector quantized diffusion model for text-to-image synthesis
Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo · 2021
Later among the works it cites.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Later among the works it cites.
Alias-free generative adversarial networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pacgan: The power of two samples in generative adversarial networks
Zinan Lin, Ashish Khetan, Giulia Fanti, and Sewoong Oh · 2018
Cited alongside, same era.
Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Cited alongside, same era.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Cited alongside, same era.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2019
Cited alongside, same era.
Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron Van den Oord, and Oriol Vinyals · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Later among the works it cites.
Gnn-lm: Language modeling based on global contexts via gnn
Yuxian Meng, Shi Zong, Xiaoya Li, Xiaofei Sun, Tianwei Zhang, Fei Wu, and Jiwei Li · 2021
Later among the works it cites.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 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
Later among the works it cites.
Projected gans converge faster
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger · 2021
Later among the works it cites.
Laion-400m: Open dataset of clip-filtered 400 million image-text pairs, 2021
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
Later among the works it cites.
Retrievalfuse: Neural 3d scene reconstruction with a database
Yawar Siddiqui, Justus Thies, Fangchang Ma, Qi Shan, Matthias Nießner, and Angela Dai · 2021
Later among the works it cites.
D2C: diffusion-denoising models for few-shot conditional generation
Abhishek Sinha, Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Later among the works it cites.
Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2021
Later among the works it cites.
Texture memory-augmented deep patch-based image inpainting
Rui Xu, Minghao Guo, Jiaqi Wang, Xiaoxiao Li, Bolei Zhou, and Chen Change Loy · 2021
Later among the works it cites.
Vector-quantized image modeling with improved vqgan
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu · 2021
Later among the works it cites.
Lafite: Towards language-free training for text-to-image generation
Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, and Tong Sun · 2021
Later among the works it cites.
Knn-diffusion: Image generation via large-scale retrieval
Oron Ashual, Shelly Sheynin, Adam Polyak, Uriel Singer, Oran Gafni, Eliya Nachmani, and Yaniv Taigman · 2022
Closest in time.
Any-resolution training for high-resolution image synthesis
Lucy Chai, Michael Gharbi, Eli Shechtman, Phillip Isola, and Richard Zhang · 2022
Closest in time.
Tweet on Classifier-free guidance for autoregressive models
Katherine Crowson · 2022
Closest in time.
The role of imagenet classes in fréchet inception distance
Tuomas Kynkäänniemi, Tero Karras, Miika Aittala, Timo Aila, and Jaakko Lehtinen · 2022
Closest in time.
Retrieval augmented classification for long-tail visual recognition
Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, and Anton van den Hengel · 2022
Closest in time.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Closest in time.
Stylegan-xl: Scaling stylegan to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
Closest in time.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Cade W Gordon, Ross Wightman, Theo Coombes, Aarush Katta, Clayton Mullis, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, et al · 2022
Closest in time.
Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, and Christian Szegedy · 2022
Closest in time.
Diffusion probabilistic modeling for video generation
Ruihan Yang, Prakhar Srivastava, and Stephan Mandt · 2022
Closest in time.