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Generative models are now capable of producing highly realistic images that look nearly indistinguishable from the data on which they are trained.
Modular learning in neural networks
Dana H Ballard · 1987
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Self-organizing neural network that discovers surfaces in random-dot stereograms
Suzanna Becker and Geoffrey E. Hinton · 1992
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Learning classification with unlabeled data
Virginia R de Sa · 1994
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Fast pose estimation with parameter-sensitive hashing
Gregory Shakhnarovich, Paul Viola, and Trevor Darrell · 2003
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Synthetic data for small area estimation
Joseph W Sakshaug and Trivellore E Raghunathan · 2010
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Stylespace analysis: Disentangled controls for stylegan image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman · 2011
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Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Flownet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick Van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
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Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, and Russell Webb · 2017
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Learning from synthetic humans
Gul Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J Black, Ivan Laptev, and Cordelia Schmid · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Logan: Membership inference attacks against generative models, 2018
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A Efros, and Trevor Darrell · 2018
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Cross-domain self-supervised multi-task feature learning using synthetic imagery
Zhongzheng Ren and Yong Jae Lee · 2018
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Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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On the ”steerability” of generative adversarial networks, 2020
Ali Jahanian, Lucy Chai, and Phillip Isola · 2020
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Niharika Jain, Alberto Olmo, Sailik Sengupta, Lydia Manikonda, and Subbarao Kambhampati · 2020
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Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach
Yuhua Chen, Wen Li, Xiaoran Chen, and Luc Van Gool · 2019
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Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
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Ganalyze: Toward visual definitions of cognitive image properties
Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola · 2019
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Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
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A style-based generator architecture for generative adversarial networks, 2019
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Supervised contrastive learning, 2020
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Generative interventions for causal learning
Chengzhi Mao, Amogh Gupta, Augustine Cha, Hao Wang, Junfeng Yang, and Carl Vondrick · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin · 2020
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Audio-visual instance discrimination with cross-modal agreement
Pedro Morgado, Nuno Vasconcelos, and Ishan Misra · 2020
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The deep bootstrap: Good online learners are good offline generalizers
Preetum Nakkiran, Behnam Neyshabur, and Hanie Sedghi · 2020
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Synthetic observations from deep generative models and binary omics data with limited sample size
Jens Nußberger, Frederic Boesel, Stefan Maria Lenz, Harald Binder, and Moritz Hess · 2020
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Multi-modal self-supervision from generalized data transformations
Mandela Patrick, Yuki M Asano, Ruth Fong, João F Henriques, Geoffrey Zweig, and Andrea Vedaldi · 2020
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Closing the ai accountability gap: Defining an end-to-end framework for internal algorithmic auditing
Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes · 2020
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Fair attribute classification through latent space de-biasing
Vikram V Ramaswamy, Sunnis SY Kim, and Olga Russakovsky · 2020
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Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2020
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Improving the fairness of deep generative models without retraining, 2020
Shuhan Tan, Yujun Shen, and Bolei Zhou · 2020
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What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
Allan Tucker, Zhenchen Wang, Ylenia Rotalinti, and Puja Myles · 2020
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What should not be contrastive in contrastive learning
Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell · 2020
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Learning to see by looking at noise
Manel Baradad, Jonas Wulff, Tongzhou Wang, Phillip Isola, and Antonio Torralba · 2021
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Ensembling with deep generative views
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Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation
August DuMont Schütte, Jürgen Hetzel, Sergios Gatidis, Tobias Hepp, Benedikt Dietz, Stefan Bauer, and Patrick Schwab · 2021
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Training GANs with stronger augmentations via contrastive discriminator
Jongheon Jeong and Jinwoo Shin · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Repurposing gans for one-shot semantic part segmentation
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Data-efficient instance generation from instance discrimination
Ceyuan Yang, Yujun Shen, Yinghao Xu, and Bolei Zhou · 2021
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