mlsanityData to 7 Oct 2026

A Two-Step Disentanglement Method

arXiv 1709.00199 ↗First posted 1 Sep 2017cs.LG stat.MLcited by 13

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Abstract (arXiv)

We address the problem of disentanglement of factors that generate a given data into those that are correlated with the labeling and those that are not. Our solution is simpler than previous solutions and employs adversarial training. First, the part of the data that is correlated with the labels is extracted by training a classifier. Then, the other part is extracted such that it enables the reconstruction of the original data but does not contain label information. The utility of the new method is demonstrated on visual datasets as well as on financial data. Our code is available at https://github.com/naamahadad/A-Two-Step-Disentanglement-Method

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2017.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 0; 2025: 2; 2026: 2.

13 papers.

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  1. 2021Understanding Object Dynamics for Interactive Image-to-Video Synthesiscited by 38
  2. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  3. 2023A Survey of Methods, Challenges and Perspectives in Causalitycited by 6
  4. 2023Disentangled Representation Learning with Transmitted Information Bottleneckcited by 3
  5. 2021Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spacescited by 2
  6. 2025Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencodercited by 2
  7. 2023Counterfactual Explanation for Regression via Disentanglement in Latent Spacecited by 1
  8. 2025Self-supervised Latent Space Optimization with Nebula Variational Codingcited by 1
  9. 2022On Disentangled and Locally Fair Representationscited by 0
  10. 20223DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulationcited by 0
  11. 2023Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Spacecited by 0
  12. 2026LCIP: Loss-Controlled Inverse Projection of High-Dimensional Image Datacited by 0
1 more
  1. 2026The Trade-off Between Covariate Dependence and Latent Structure in Representation Learningcited by 0