mlsanityData to 7 Oct 2026

Disentangling Autoencoders (DAE)

arXiv 2202.09926 ↗First posted 20 Feb 2022cs.LG cs.AIcited by 3

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Abstract (arXiv)

Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory. To the best of our knowledge, this is the first deterministic model that is aiming to achieve disentanglement based on autoencoders without regularizers. The proposed model is compared to seven state-of-the-art generative models based on autoencoders and evaluated based on five supervised disentanglement metrics. The experimental results show that the proposed model can have better disentanglement when variances of each features are different. We believe that this model leads to a new field for disentanglement learning based on autoencoders without regularizers.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 28 of its 59 references that are in the map. First posted on arXiv 20 Feb 2022.

Builds on

28 of 59 references.

References · 28

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  4. 2015Variational Inference with Normalizing Flowscited by 2,161
  5. 2018Disentangling by Factorisingcited by 614
  6. 2017Wasserstein Auto-Encoderscited by 371
  7. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  8. 2018Understanding disentangling in β-VAEcited by 332
  9. 2018Towards a Definition of Disentangled Representationscited by 294
  10. 2017VAE with a VampPriorcited by 247
  11. 2018Isolating Sources of Disentanglement in Variational Autoencoderscited by 167
  12. 2018Recent Advances in Autoencoder-Based Representation Learningcited by 130
16 more
  1. 2018Discovering physical concepts with neural networkscited by 117
  2. 2018Learning Deep Disentangled Embeddings with the F-Statistic Losscited by 101
  3. 2021Counterfactual Zero-Shot and Open-Set Visual Recognitioncited by 95
  4. 2018Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizercited by 87
  5. 2020Measuring Disentanglement: A Review of Metricscited by 69
  6. 2020A Contrastive Learning Approach for Training Variational Autoencoder Priorscited by 65
  7. 2020Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANscited by 63
  8. 2019Disentangling Factors of Variation Using Few Labelscited by 57
  9. 2019Theory and Evaluation Metrics for Learning Disentangled Representationscited by 57
  10. 2017Learning Independent Features with Adversarial Nets for Non-linear ICAcited by 33
  11. 2018Variational Autoencoder with Implicit Optimal Priorscited by 33
  12. 2020Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neuronscited by 33
  13. 2020Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoderscited by 30
  14. 2016A Survey of Inductive Biases for Factorial Representation-Learningcited by 23
  15. 2019Perceptual Generative Autoencoderscited by 15
  16. 2019Non-linear ICA based on Cramer-Wold metriccited by 3

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Robust Anomaly Detection through Multi-Modal Autoencoder Fusion for Small Vehicle Damage Detectioncited by 1
  2. 2025Intrinsic Dimension Estimating Autoencoder (IDEA) Using CancelOut Layer and a Projected Losscited by 1
  3. 2024StyleAutoEncoder for manipulating image attributes using pre-trained StyleGANcited by 0