mlsanityData to 14 Oct 2026

Learning Disentangled Representations via Mutual Information Estimation

arXiv 1912.03915 ↗First posted 9 Dec 2019stat.ML cs.LGcited by 52

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Abstract (arXiv)

In this paper, we investigate the problem of learning disentangled representations. Given a pair of images sharing some attributes, we aim to create a low-dimensional representation which is split into two parts: a shared representation that captures the common information between the images and an exclusive representation that contains the specific information of each image. To address this issue, we propose a model based on mutual information estimation without relying on image reconstruction or image generation. Mutual information maximization is performed to capture the attributes of data in the shared and exclusive representations while we minimize the mutual information between the shared and exclusive representation to enforce representation disentanglement. We show that these representations are useful to perform downstream tasks such as image classification and image retrieval based on the shared or exclusive component. Moreover, classification results show that our model outperforms the state-of-the-art model based on VAE/GAN approaches in representation disentanglement.

Key facts. Cited by 52 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Dec 2019.

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Citing papers per year: 2021: 8; 2022: 11; 2023: 9; 2024: 12; 2025: 6; 2026: 6.

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  1. 2022EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equationscited by 172
  2. 2022Disentangled Representation Learningcited by 112
  3. 2021Disentangling Hate in Online Memescited by 46
  4. 2024Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Predictioncited by 41
  5. 2023Multimodal Variational Auto-encoder based Audio-Visual Segmentationcited by 38
  6. 2021A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representationscited by 37
  7. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  8. 2023Generalizing Graph ODE for Learning Complex System Dynamics across Environmentscited by 23
  9. 2023DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropiescited by 21
  10. 2024Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detectioncited by 16
  11. 2024An Information Criterion for Controlled Disentanglement of Multimodal Datacited by 14
  12. 2022Improving Adversarial Robustness via Mutual Information Estimationcited by 13
38 more
  1. 2022Gacs-Korner Common Information Variational Autoencodercited by 12
  2. 2025STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classificationcited by 12
  3. 2025DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-IDcited by 11
  4. 2023CLAP: Isolating Content from Style through Contrastive Learning with Augmented Promptscited by 10
  5. 2024Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformerscited by 10
  6. 2024DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Datacited by 10
  7. 2022Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognitioncited by 9
  8. 2023Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detectioncited by 9
  9. 2023Supervised structure learningcited by 9
  10. 2022Content-Context Factorized Representations for Automated Speech Recognitioncited by 8
  11. 2021Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximizationcited by 5
  12. 2021On the relationship between disentanglement and multi-task learningcited by 4
  13. 2023Active Inference and Intentional Behaviourcited by 4
  14. 2021Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervisioncited by 3
  15. 2021Disentanglement by Cyclic Reconstructioncited by 3
  16. 2023Disentangled Representation Learning with Transmitted Information Bottleneckcited by 3
  17. 2024Constrained Multiview Representation for Self-supervised Contrastive Learningcited by 3
  18. 2024Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defensecited by 3
  19. 2024Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communicationcited by 3
  20. 2025Active inference and artificial reasoningcited by 3
  21. 2021Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networkscited by 2
  22. 2022Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garmentcited by 2
  23. 2024Comparing the information content of probabilistic representation spacescited by 2
  24. 2024Symbolic Disentangled Representations for Imagescited by 2
  25. 2022Neural Topic Modeling with Deep Mutual Information Estimationcited by 1
  26. 2022Break The Spell Of Total Correlation In betaTCVAEcited by 1
  27. 2023Learning from Multiple Sources for Data-to-Text and Text-to-Datacited by 1
  28. 2024Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Contentcited by 1
  29. 20252D Gaussians Meet Visual Tokenizercited by 1
  30. 2025DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalitiescited by 1
  31. 2026DRGW: Learning Disentangled Representations for Robust Graph Watermarkingcited by 1
  32. 2026Diff-CA: Separating Common and Salient Factors with Diffusion Modelscited by 1
  33. 2022Just Noticeable Difference Modeling for Face Recognition Systemcited by 0
  34. 2022Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factorscited by 0
  35. 2024CrossSpeech++: Cross-lingual Speech Synthesis with Decoupled Language and Speaker Generationcited by 0
  36. 2025GED-Consistent Disentanglement of Aligned and Unaligned Substructures for Graph Similarity Learningcited by 0
  37. 2026Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalizationcited by 0
  38. 2026NMINE: Normalized Mutual Information Neural Estimationcited by 0

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