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We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort.
Active learning for probability estimation using jensen-shannon divergence
Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, and Raymond Mooney · 2005
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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To recognize shapes, first learn to generate images
Geoffrey E. Hinton · 2007
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Labelme: A database and web-based tool for image annotation
Bryan Russell, Antonio Torralba, Kevin Murphy, and William Freeman · 2008
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Cat head detection - how to effectively exploit shape and texture features
Weiwei Zhang, Jian Sun, and Xiaoou Tang · 2008
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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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 nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, M. Maire, Serge J. Belongie, James Hays, P. Perona, D. Ramanan, Piotr Dollár, and C. L. Zitnick · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
G. Van Horn, S. Branson, R. Farrell, S. Haber, J. Barry, P. Ipeirotis, P. Perona, and S. Belongie · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semantic segmentation using adversarial networks
Pauline Luc, Camille Couprie, Soumith Chintala, and Jakob Verbeek · 2016
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Semantic understanding of scenes through the ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2016
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Generating visual representations for zero-shot classification
Maxime Bucher, Stéphane Herbin, and Frédéric Jurie · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Zero-shot learning using synthesised unseen visual data with diffusion regularisation
Yang Long, Li Liu, Fumin Shen, Ling Shao, and Xuelong Li · 2017
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A geometric approach to active learning for convolutional neural networks
O. Sener and S. Savarese · 2017
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Semi supervised semantic segmentation using generative adversarial network
Nasim Souly, Concetto Spampinato, and Mubarak Shah · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Meta-sim: Learning to generate synthetic datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, and Sanja Fidler · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Semi-supervised semantic segmentation with high-and low-level consistency
Sudhanshu Mittal, Maxim Tatarchenko, and Thomas Brox · 2019
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Gradient matching generative networks for zero-shot learning
Mert Bulent Sariyildiz and Ramazan Gokberk Cinbis · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
Cited alongside, same era.
The power of ensembles for active learning in image classification
W. H. Beluch, T. Genewein, A. Nurnberger, and J. M. Kohler · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
Multi-modal cycle-consistent generalized zero-shot learning
Rafael Felix, Vijay BG Kumar, Ian Reid, and Gustavo Carneiro · 2018
Cited alongside, same era.
Adversarial learning for semi-supervised semantic segmentation
Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang · 2018
Cited alongside, same era.
Cost-sensitive active learning for intracranial hemorrhage detection
Weicheng Kuo, Christian Häne, E. Yuh, P. Mukherjee, and Jitendra Malik · 2018
Cited alongside, same era.
Image to image translation for domain adaptation
Zak Murez, Soheil Kolouri, David Kriegman, Ravi Ramamoorthi, and Kyungnam Kim · 2018
Cited alongside, same era.
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton · 2020
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Meta-sim2: Unsupervised learning of scene structure for synthetic data generation
Jeevan Devaranjan, Amlan Kar, and Sanja Fidler · 2020
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Learning high-resolution domain-specific representations with a gan generator
Danil Galeev, Konstantin Sofiiuk, Danila Rukhovich, Mikhail Romanov, Olga Barinova, and Anton Konushin · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 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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Guided collaborative training for pixel-wise semi-supervised learning
Zhanghan Ke, Di Qiu, Kaican Li, Qiong Yan, and Rynson WH Lau · 2020
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Federated simulation for medical imaging
Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F Frangi, and Sanja Fidler · 2020
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Variational amodal object completion for interactive scene editing
Huan Ling, David Acuna, Karsten Kreis, Seung Kim, and Sanja Fidler · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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Image gans meet differentiable rendering for inverse graphics and interpretable 3d neural rendering
Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, and Sanja Fidler · 2020
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Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization
Daiqing Li, Junlin Yang, Karsten Kreis, Antonio Torralba, and Sanja Fidler · 2021
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