Fetching the paper…
Reading the bibliography…
Representation learners that disentangle factors of variation have already proven to be important in addressing various real world concerns such as fairness and interpretability.
Counterfactual visual explanations
Goyal, Y.; Wu, Z.; Ernst, J.; Batra, D.; Parikh, D.; and Lee, S. 2019b · 1904
Earlier work this paper cites.
Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology
Zmigrod, R.; Mielke, S. J.; Wallach, H.; and Cotterell, R. 2019 · 1906
Earlier work this paper cites.
Explaining classifiers with causal concept effect (cace)
Goyal, Y.; Feder, A.; Shalit, U.; and Kim, B. 2019a · 1907
Earlier work this paper cites.
Weakly-Supervised Disentanglement Without Compromises
Locatello, F.; Poole, B.; Rätsch, G.; Schölkopf, B.; Bachem, O.; and Tschannen, M. 2020 · 2002
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y.; Huang, F. J.; and Bottou, L. 2004 · 2004
Earlier work this paper cites.
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Yang, M.; Liu, F.; Chen, Z.; Shen, X.; Hao, J.; and Wang, J. 2020 · 2004
Earlier work this paper cites.
Counterfactual Data Augmentation using Locally Factored Dynamics
Pitis, S.; Creager, E.; and Garg, A. 2020 · 2007
Earlier work this paper cites.
Causality
Pearl, J. 2009 · 2009
Earlier work this paper cites.
3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model
Fidler, S.; Dickinson, S.; and Urtasun, R. 2012 · 2012
Earlier work this paper cites.
Representation Learning: A Review and New Perspectives
Bengio, Y.; Courville, A.; and Vincent, P. 2013 · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
Earlier work this paper cites.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D. J.; Mohamed, S.; and Wierstra, D. 2014 · 2014
Earlier work this paper cites.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Higgins, I.; Matthey, L.; Pal, A.; Burgess, C.; Glorot, X.; Botvinick, M. M.; Mohamed, S.; and Lerchner, A. 2017 · 2017
Earlier work this paper cites.
Variational inference of disentangled latent concepts from unlabeled observations
Kumar, A.; Sattigeri, P.; and Balakrishnan, A. 2017 · 2017
Earlier work this paper cites.
Elements of Causal Inference: Foundations and Learning Algorithms
Peters, J.; Janzing, D.; and Schölkopf, B. 2017 · 2017
Earlier work this paper cites.
Explaining image classifiers by counterfactual generation
Chang, C.-H.; Creager, E.; Goldenberg, A.; and Duvenaud, D. 2018 · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Chen, R. T.; Li, X.; Grosse, R. B.; and Duvenaud, D. K. 2018 · 2018
Cited alongside, same era.
A framework for the quantitative evaluation of disentangled representations
Eastwood, C.; and Williams, C. K. 2018 · 2018
Cited alongside, same era.
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
Gilpin, L. H.; Bau, D.; Yuan, B. Z.; Bajwa, A.; Specter, M.; and Kagal, L. 2018 · 2018
Cited alongside, same era.
Kim, H.; and Mnih, A. 2018 · 2018
Cited alongside, same era.
Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness
Suter, R.; Miladinovic, D.; Schölkopf, B.; and Bauer, S. 2019 · 2019
Later among the works it cites.
Downsampled Disentanglement Datasets - Falcor3D and Isaac3D
Anonymous. 2020 · 2020
Later among the works it cites.
3D Shapes Dataset
Burgess, C.; and Kim, H. 2018 · 2020
Later among the works it cites.
Blender - a 3D modelling and rendering package
Community, B. O. 2018 · 2020
Later among the works it cites.
Diva: Domain invariant variational autoencoders
Ilse, M.; Tomczak, J. M.; Louizos, C.; and Welling, M. 2020 · 2020
Later among the works it cites.
dSprites: Disentanglement testing Sprites dataset
Matthey, L.; Higgins, I.; Hassabis, D.; and Lerchner, A. 2017 · 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…
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
Kocaoglu, M.; Snyder, C.; Dimakis, A. G.; and Vishwanath, S. 2018 · 2018
Cited alongside, same era.
The mythos of model interpretability
Lipton, Z. C. 2018 · 2018
Cited alongside, same era.
Learning deep disentangled embeddings with the f-statistic loss
Ridgeway, K.; and Mozer, M. C. 2018 · 2018
Cited alongside, same era.
Neural Network Attributions: A Causal Perspective
Chattopadhyay, A.; Manupriya, P.; Sarkar, A.; and Balasubramanian, V. N. 2019 · 2019
Cited alongside, same era.
Flexibly fair representation learning by disentanglement
Creager, E.; Madras, D.; Jacobsen, J.-H.; Weis, M.; Swersky, K.; Pitassi, T.; and Zemel, R. 2019 · 2019
Cited alongside, same era.
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Gondal, M. W.; Wuthrich, M.; Miladinovic, D.; Locatello, F.; Breidt, M.; Volchkov, V.; Akpo, J.; Bachem, O.; Schölkopf, B.; and Bauer, S. 2019 · 2019
Cited alongside, same era.
Causal Inference
Hernan, M.; and Robins, J. 2019 · 2019
Cited alongside, same era.
Generative causal explanations of black-box classifiers
O' Shaughnessy, M.; Canal, G.; Connor, M.; Rozell, C.; and Davenport, M. 2020 · 2020
Later among the works it cites.
Weakly Supervised Disentanglement with Guarantees
Shu, R.; Chen, Y.; Kumar, A.; Ermon, S.; and Poole, B. 2020 · 2020
Later among the works it cites.
Causal Discovery with Reinforcement Learning
Zhu, S.; Ng, I.; and Chen, Z. 2020 · 2020
Later among the works it cites.
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
Zhu, Y.; Min, M. R.; Kadav, A.; and Graf, H. P. 2020 · 2020
Later among the works it cites.
On the Transfer of Disentangled Representations in Realistic Settings
Dittadi, A.; Träuble, F.; Locatello, F.; Wuthrich, M.; Agrawal, V.; Winther, O.; Bauer, S.; and Schölkopf, B. 2021 · 2021
Closest in time.
The role of Disentanglement in Generalisation
Montero, M. L.; Ludwig, C. J.; Costa, R. P.; Malhotra, G.; and Bowers, J. 2021 · 2021
Closest in time.
Toward Causal Representation Learning
Schölkopf, B.; Locatello, F.; Bauer, S.; Ke, N. R.; Kalchbrenner, N.; Goyal, A.; and Bengio, Y. 2021 · 2021
Closest in time.
On Disentangled Representations Learned from Correlated Data
Träuble, F.; Creager, E.; Kilbertus, N.; Locatello, F.; Dittadi, A.; Goyal, A.; Schölkopf, B.; and Bauer, S. 2021 · 2021
Closest in time.