mlsanityData to 14 Oct 2026

Learning Independent Features with Adversarial Nets for Non-linear ICA

arXiv 1710.05050 ↗First posted 13 Oct 2017stat.MLcited by 33

Not on the map. Why?

Abstract (arXiv)

Reliable measures of statistical dependence could be useful tools for learning independent features and performing tasks like source separation using Independent Component Analysis (ICA). Unfortunately, many of such measures, like the mutual information, are hard to estimate and optimize directly. We propose to learn independent features with adversarial objectives which optimize such measures implicitly. These objectives compare samples from the joint distribution and the product of the marginals without the need to compute any probability densities. We also propose two methods for obtaining samples from the product of the marginals using either a simple resampling trick or a separate parametric distribution. Our experiments show that this strategy can easily be applied to different types of model architectures and solve both linear and non-linear ICA problems.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Oct 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 8; 2023: 2; 2024: 5; 2025: 5; 2026: 2.

33 papers.

Sort:

By area:

  1. 2021AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defensecited by 10
  2. 2022Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representationscited by 10
  3. 2024DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Datacited by 10
  4. 2021A Normative and Biologically Plausible Algorithm for Independent Component Analysiscited by 8
  5. 2022Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfercited by 8
  6. 2021Innovations Autoencoder and its Application in One-class Anomalous Sequence Detectioncited by 7
  7. 2021CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervisioncited by 7
  8. 2024Causal Contrastive Learning for Counterfactual Regression Over Timecited by 7
  9. 2021Estimating and Maximizing Mutual Information for Knowledge Distillationcited by 6
  10. 2021PluGeN: Multi-Label Conditional Generation From Pre-Trained Modelscited by 5
  11. 2025The Hidden Link Between RLHF and Contrastive Learningcited by 5
  12. 2021On the relationship between disentanglement and multi-task learningcited by 4
21 more
  1. 2021Representation Learning for Conversational Data using Discourse Mutual Information Maximizationcited by 4
  2. 2022A robust estimator of mutual information for deep learning interpretabilitycited by 4
  3. 2024An Independence-promoting Loss for Music Generation with Language Modelscited by 4
  4. 2021Discovery of Single Independent Latent Variablecited by 3
  5. 2022Deep Deterministic Independent Component Analysis for Hyperspectral Unmixingcited by 3
  6. 2022Disentangling Autoencoders (DAE)cited by 3
  7. 2024Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defensecited by 3
  8. 2021Single-preparation unsupervised quantum machine learning: concepts and applicationscited by 2
  9. 2023Non-parametric Probabilistic Time Series Forecasting via Innovations Representationcited by 2
  10. 2025Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysiscited by 2
  11. 2025Structured Kernel Regression VAE: A Computationally Efficient Surrogate for GP-VAEs in ICAcited by 2
  12. 2022Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approachcited by 1
  13. 2023Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoderscited by 1
  14. 2025Adversarial Dependence Minimizationcited by 1
  15. 2026Refining the Information Bottleneck via Adversarial Information Separationcited by 1
  16. 2021Weakly Supervised Recovery of Semantic Attributescited by 0
  17. 2022Visual processing in context of reinforcement learningcited by 0
  18. 2022Semi-Blind Source Separation with Learned Constraintscited by 0
  19. 2024Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologiescited by 0
  20. 2025Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysiscited by 0
  21. 2026The Trade-off Between Covariate Dependence and Latent Structure in Representation Learningcited by 0