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Obtaining the human-like perception ability of abstracting visual concepts from concrete pixels has always been a fundamental and important target in machine learning research fields such as disentangled representation learning and scene decomposition.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Deep visual analogy-making
Scott E. Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 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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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Improving the robustness of deep neural networks via stability training
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Machine learning on big data: Opportunities and challenges
Lina Zhou, Shimei Pan, Jianwu Wang, and Athanasios V Vasilakos · 2017
Earlier work this paper cites.
Towards robust interpretability with self-explaining neural networks
David Alvarez Melis and Tommi Jaakkola · 2018
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Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
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Understanding disentangling in beta-vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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Isolating sources of disentanglement in variational autoencoders
Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher K. I. Williams · 2018
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Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
Cited alongside, same era.
Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
Cited alongside, same era.
Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Unsupervised discovery of interpretable directions in the GAN latent space
Andrey Voynov and Artem Babenko · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Unsupervised learning of compositional energy concepts
Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, and Igor Mordatch · 2021
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Unsupervised learning of compositional energy concepts
Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, and Igor Mordatch · 2021
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Seeing out of the box: End-to-end pre-training for vision-language representation learning
Zhicheng Huang, Zhaoyang Zeng, Yupan Huang, Bei Liu, Dongmei Fu, and Jianlong Fu · 2021
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Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2019
Cited alongside, same era.
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
Cited alongside, same era.
Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
Cited alongside, same era.
Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
Cited alongside, same era.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
Cited alongside, same era.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
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Disentangled representations from non-disentangled models
Valentin Khrulkov, Leyla Mirvakhabova, Ivan V. Oseledets, and Artem Babenko · 2021
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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
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Learning disentangled representation by exploiting pretrained generative models: A contrastive learning view
Xuanchi Ren, Tao Yang, Yuwang Wang, and Wenjun Zeng · 2021
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Closed-form factorization of latent semantics in gans
Yujun Shen and Bolei Zhou · 2021
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Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
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Towards building a group-based unsupervised representation disentanglement framework
Tao Yang, Xuanchi Ren, Yuwang Wang, Wenjun Zeng, and Nanning Zheng · 2021
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Retriever: Learning content-style representation as a token-level bipartite graph
Dacheng Yin, Xuanchi Ren, Chong Luo, Yuwang Wang, Zhiwei Xiong, and Wenjun Zeng · 2021
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Towards self-supervised learning of global and object-centric representations
Federico Baldassarre and Hossein Azizpour · 2022
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