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Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions.
Statistics and Causal Inference
Paul W. Holland · 1986
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, and and · 2009
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Understanding egocentric activities
Alireza Fathi, Ali Farhadi, and James M. Rehg · 2011
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On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
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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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Neural Expectation Maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2017
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Beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Earlier work this paper cites.
Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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David Ha and Jürgen Schmidhuber · 2018
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Learning Independent Causal Mechanisms
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, and Bernhard Schölkopf · 2018
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MONet: Unsupervised Scene Decomposition and Representation, January 2019
Christopher P. Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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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
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Variational Autoencoders Pursue PCA Directions (by Accident)
Michal Rolinek, Dominik Zietlow, and Georg Martius · 2019
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Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
Cited alongside, same era.
A causal view of compositional zero-shot recognition
Yuval Atzmon, Felix Kreuk, Uri Shalit, and Gal Chechik · 2020
Cited alongside, same era.
On the Transfer of Disentangled Representations in Realistic Settings
Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf · 2020
Cited alongside, same era.
Learning to Manipulate Individual Objects in an Image
Yanchao Yang, Yutong Chen, and Stefano Soatto · 2020
Cited alongside, same era.
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernhard Schölkopf, and Stefan Bauer · 2021
Cited alongside, same era.
Recurrent Independent Mechanisms
Contrastive Learning Inverts the Data Generating Process
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel · 2021
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Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe, and Taco Cohen · 2022
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Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation
Michael Chang, Thomas L. Griffiths, and Sergey Levine · 2022
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Towards a Grounded Theory of Causation for Embodied AI, June 2022
Taco Cohen · 2022
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Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100
Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray · 2022
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Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, and Bernhard Schölkopf · 2021
Cited alongside, same era.
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dylan Paiton · 2021
Cited alongside, same era.
The role of Disentanglement in Generalisation
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers · 2021
Cited alongside, same era.
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, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
Independent Prototype Propagation for Zero-Shot Compositionality
Frank Ruis, Gertjan Burghouts, and Doina Bucur · 2021
Cited alongside, same era.
Towards Causal Representation Learning, February 2021
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
Cited alongside, same era.
On Disentangled Representations Learned from Correlated Data
Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer · 2021
Cited alongside, same era.
Generalization and Robustness Implications in Object-Centric Learning
Andrea Dittadi, Samuele S. Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello · 2022
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Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
Sebastien Lachapelle, Pau Rodriguez, Yash Sharma, Katie E. Everett, Rémi LE Priol, Alexandre Lacoste, and Simon Lacoste-Julien · 2022
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Variational Causal Dynamics: Discovering Modular World Models from Interventions, June 2022
Anson Lei, Bernhard Schölkopf, and Ingmar Posner · 2022
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iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, and Efstratios Gavves · 2022
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Intervention Design for Causal Representation Learning
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, and Efstratios Gavves · 2022
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Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective
Yuejiang Liu, Riccardo Cadei, Jonas Schweizer, Sherwin Bahmani, and Alexandre Alahi · 2022
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Bridging the Gap to Real-World Object-Centric Learning, September 2022
Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, and Francesco Locatello · 2022
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Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos
Gautam Singh, Yi-Fu Wu, and Sungjin Ahn · 2022
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GroupViT: Semantic Segmentation Emerges From Text Supervision
Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang · 2022
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