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Unsupervised fine-grained class clustering is a practical yet challenging task due to the difficulty of feature representations learning of subtle object details.
The hungarian method for the assignment problem
H. W. Kuhn and Bryn Yaw · 1955
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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An analysis of single layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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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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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Unsupervised deep embedding for clustering analysis
Junyuan Xie, Ross B. Girshick, and Ali Farhadi · 2016
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Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization
Kamran Ghasedi Dizaji, Amirhossein Herandi, and Heng Huang · 2017
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Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Nsml: A machine learning platform that enables you to focus on your models
Nako Sung, Minkyu Kim, Hyunwoo Jo, Youngil Yang, Jingwoong Kim, Leonard Lausen, Youngkwan Kim, Gayoung Lee, Donghyun Kwak, Jung-Woo Ha, and Sunghun Kim · 2017
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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 Metaxas · 2017
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Large scale fine-grained categorization and domain-specific transfer learning
Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Belongie Serge · 2018
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Nsml: Meet the mlaas platform with a real-world case study
Hanjoo Kim, Minkyu Kim, Dongjoo Seo, Jinwoong Kim, Heungseok Park, Soeun Park, Hyunwoo Jo, KyungHyun Kim, Youngil Yang, Youngkwan Kim, et al · 2018
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The science of fake news
David MJ Lazer, Matthew A Baum, Yochai Benkler, Adam J Berinsky, Kelly M Greenhill, Filippo Menczer, Miriam J Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, et al · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Emergence of object segmentation in perturbed generative models
Adam Bielski and Paolo Favaro · 2019
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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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Mixnmatch: Multifactor disentanglement and encoding for conditional image generation
Yuheng Li, Krishna Kumar Singh, Utkarsh Ojha, and Yong Jae Lee · 2020
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Diverse image generation via self-conditioned gans
Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, and Antonio Torralba · 2020
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An overview of voice conversion and its challenges: From statistical modeling to deep learning
Berrak Sisman, Junichi Yamagishi, Simon King, and Haizhou Li · 2020
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Scan: Learning to classify images without labels
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 2020
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Mickaël Chen, Thierry Artières, and Ludovic Denoyer · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, João F Henriques, and Andrea Vedaldi · 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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Finegan: Unsupervised hierarchical disentanglement for fine-grained object generation and discovery
Krishna Kumar Singh, Utkarsh Ojha, and Yong Jae Lee · 2019
Cited alongside, same era.
Unsupervised fake news detection on social media: A generative approach
Shuo Yang, Kai Shu, Suhang Wang, Renjie Gu, Fan Wu, and Huan Liu · 2019
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Onegan: Simultaneous unsupervised learning of conditional image generation, foreground segmentation, and fine-grained clustering
Yaniv Benny and Lior Wolf · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Andrey Voynov and Artem Babenko · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Deep robust clustering by contrastive learning
Huasong Zhong, Chong Chen, Zhongming Jin, and Xian-Sheng Hua · 2020
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Labels4free: Unsupervised segmentation using stylegan
Rameen Abdal, Peihao Zhu, Niloy J. Mitra, and Peter Wonka · 2021
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Transitioning from real to synthetic data: Quantifying the bias in model
Aman Gupta, Deepak Bhatt, and Anubha Pandey · 2021
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What changes can large-scale language models bring? intensive study on hyperclova: Billions-scale korean generative pretrained transformers
Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Dong Hyeon Jeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, et al · 2021
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, and Steven C.H. Hoi · 2021
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Finding an unsupervised image segmenter in each of your deep generative models
Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, and Andrea Vedaldi · 2021
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Information-theoretic segmentation by inpainting error maximization
Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich, and David McAllester · 2021
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A good image generator is what you need for high-resolution video synthesis
Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris N Metaxas, and Sergey Tulyakov · 2021
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Videogpt: Video generation using vq-vae and transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas · 2021
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