Fetching the paper…
Reading the bibliography…
The advent of large-scale training has produced a cornucopia of powerful visual recognition models.
Ensemble selection from libraries of models
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes · 2004
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.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
Earlier work this paper cites.
Learning hybrid image templates (hit) by information projection
Zhangzhang Si and Song-Chun Zhu · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Earlier work this paper cites.
Generating images with perceptual similarity metrics based on deep networks
Alexey Dosovitskiy and Thomas Brox · 2016
Earlier work this paper cites.
Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
What makes imagenet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016
Earlier work this paper cites.
Wasserstein Generative Adversarial Networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Photographic image synthesis with cascaded refinement networks
Qifeng Chen and Vladlen Koltun · 2017
Earlier work this paper cites.
Generative multi-adversarial networks
Ishan Durugkar, Ian Gemp, and Sridhar Mahadevan · 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.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Earlier work this paper cites.
Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2017
Earlier work this paper cites.
Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S Zemel · 2017
Earlier work this paper cites.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
Earlier work this paper cites.
Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas · 2017
Earlier work this paper cites.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A Efros · 2017
Earlier work this paper cites.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Cited alongside, same era.
Demystifying mmd gans
Mikołaj Bińkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton · 2018
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 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.
cgans with projection discriminator
Takeru Miyato and Masanori Koyama · 2018
Cited alongside, same era.
Logo synthesis and manipulation with clustered generative adversarial networks
Alexander Sage, Eirikur Agustsson, Radu Timofte, and Luc Van Gool · 2018
Cited alongside, same era.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Later among the works it cites.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Later among the works it cites.
Maskgan: Towards diverse and interactive facial image manipulation
Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo · 2020
Later among the works it cites.
Data instance prior (disp) in generative adversarial networks
Puneet Mangla, Nupur Kumari, Mayank Singh, Balaji Krishnamurthy, and Vineeth N Balasubramanian · 2020
Later among the works it cites.
Freeze the discriminator: a simple baseline for fine-tuning gans
Sangwoo Mo, Minsu Cho, and Jinwoo Shin · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Image manipulation with perceptual discriminators
Diana Sungatullina, Egor Zakharov, Dmitry Ulyanov, and Victor Lempitsky · 2018
Cited alongside, same era.
High-resolution image synthesis and semantic manipulation with conditional gans
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
Cited alongside, same era.
Transferring gans: generating images from limited data
Yaxing Wang, Chenshen Wu, Luis Herranz, Joost van de Weijer, Abel Gonzalez-Garcia, and Bogdan Raducanu · 2018
Cited alongside, same era.
Unified perceptual parsing for scene understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun · 2018
Cited alongside, same era.
Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese · 2018
Cited alongside, same era.
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2020
Later among the works it cites.
A u-net based discriminator for generative adversarial networks
Edgar Schonfeld, Bernt Schiele, and Anna Khoreva · 2020
Later among the works it cites.
Interfacegan: Interpreting the disentangled face representation learned by gans
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou · 2020
Later among the works it cites.
Semantic pyramid for image generation
Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T Freeman, and Tali Dekel · 2020
Later among the works it cites.
Towards good practices for data augmentation in gan training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, and Ngai-Man Cheung · 2020
Later among the works it cites.
Cnn-generated images are surprisingly easy to spot… for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros · 2020
Later among the works it cites.
Minegan: effective knowledge transfer from gans to target domains with few images
Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, and Joost van de Weijer · 2020
Later among the works it cites.
Inclusive gan: Improving data and minority coverage in generative models
Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, and Mario Fritz · 2020
Later among the works it cites.
Consistency regularization for generative adversarial networks
Han Zhang, Zizhao Zhang, Augustus Odena, and Honglak Lee · 2020
Later among the works it cites.
On leveraging pretrained gans for generation with limited data
Miaoyun Zhao, Yulai Cong, and Lawrence Carin · 2020
Later among the works it cites.
Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
Later among the works it cites.
Image augmentations for gan training
Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, and Han Zhang · 2020
Later among the works it cites.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Closest in time.
Instance-conditioned gan
Arantxa Casanova, Marlène Careil, Jakob Verbeek, Michal Drozdzal, and Adriana Romero · 2021
Closest in time.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Closest in time.
Detail me more: Improving gan’s photo-realism of complex scenes
Raghudeep Gadde, Qianli Feng, and Aleix M Martinez · 2021
Closest in time.
Lofgan: Fusing local representations for few-shot image generation
Zheng Gu, Wenbin Li, Jing Huo, Lei Wang, and Yang Gao · 2021
Closest in time.
Alias-free generative adversarial networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
Closest in time.
Towards faster and stabilized gan training for high-fidelity few-shot image synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal · 2021
Closest in time.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Closest in time.
On self-supervised image representations for gan evaluation
Stanislav Morozov, Andrey Voynov, and Artem Babenko · 2021
Closest in time.
Few-shot image generation via cross-domain correspondence
Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A Efros, Yong Jae Lee, Eli Shechtman, and Richard Zhang · 2021
Closest in time.
On buggy resizing libraries and surprising subtleties in fid calculation
Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu · 2021
Closest in time.
Styleclip: Text-driven manipulation of stylegan imagery
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski · 2021
Closest in time.
Scalable transfer learning with expert models
Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cedric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, and Neil Houlsby · 2021
Closest in time.
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
Closest in time.
Enhancing photorealism enhancement
Stephan R Richter, Hassan Abu AlHaija, and Vladlen Koltun · 2021
Closest in time.
Projected gans converge faster
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger · 2021
Closest in time.
Self-supervised learning with swin transformers
Zhenda Xie, Yutong Lin, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao, and Han Hu · 2021
Closest in time.
Data-efficient instance generation from instance discrimination
Ceyuan Yang, Yujun Shen, Yinghao Xu, and Bolei Zhou · 2021
Closest in time.
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
Closest in time.
Barbershop: Gan-based image compositing using segmentation masks, 2021
Peihao Zhu, Rameen Abdal, John Femiani, and Peter Wonka · 2021
Closest in time.
The role of imagenet classes in fr
Tuomas Kynkäänniemi, Tero Karras, Miika Aittala, Timo Aila, and Jaakko Lehtinen · 2022
Closest in time.