mlsanityData to 14 Oct 2026

Semi-Conditional Normalizing Flows for Semi-Supervised Learning

arXiv 1905.00505 ↗First posted 1 May 2019stat.ML cs.LGcited by 15

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Abstract (arXiv)

This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels. Semi-conditional architecture of the model allows us to efficiently compute a value and gradients of the marginal likelihood for unlabeled objects. The conditional part of the model is based on a proposed conditional coupling layer. We demonstrate performance of the model for semi-supervised classification problem on different datasets. The model outperforms the baseline approach based on variational auto-encoders on MNIST dataset.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2019.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 3; 2024: 4; 2025: 1.

15 papers.

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  1. 2021Task-agnostic Continual Learning with Hybrid Probabilistic Modelscited by 16
  2. 2021LiftPool: Bidirectional ConvNet Poolingcited by 11
  3. 2022Denoising Deep Generative Modelscited by 6
  4. 2023Lagrangian Flow Networks for Conservation Lawscited by 5
  5. 2023Conditional Matrix Flows for Gaussian Graphical Modelscited by 5
  6. 2021Flow-based Spatio-Temporal Structured Prediction of Motion Dynamicscited by 3
  7. 2024This Probably Looks Exactly Like That: An Invertible Prototypical Networkcited by 3
  8. 2025Learning Geometry and Topology via Multi-Chart Flowscited by 3
  9. 2024Generative vs. Discriminative modeling under the lens of uncertainty quantificationcited by 2
  10. 2021Semi-supervised Learning with Missing Values Imputationcited by 1
  11. 2021Flow Plugin Network for conditional generationcited by 1
  12. 2022WeaNF: Weak Supervision with Normalizing Flowscited by 0
3 more
  1. 2023Weakly Supervised Label Learning Flowscited by 0
  2. 2024LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flowcited by 0
  3. 2024Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximizationcited by 0