Papers › Semi-Supervised Learning with Normalizing Flows
Semi-Supervised Learning with Normalizing Flows
Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood. We propose FlowGMM, an end-to-end approach to generative semi supervised learning with normalizing flows, using a latent Gaussian mixture model. FlowGMM is distinct in its simplicity, unified treatment of labelled and unlabelled data with an exact likelihood, interpretability, and broad applicability beyond image data. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data, tabular data, and semi-supervised image classification. We also show that FlowGMM can discover interpretable structure, provide real-time optimization-free feature visualizations, and specify well calibrated predictive distributions.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Text Classification | AG News (200 Labels) | FlowGMM | Accuracy (%) | 82.1 | #1 of 3 | Archive leaderboard | report |
| Semi-Supervised Text Classification | AG News (200 Labels) | Pi Model | Accuracy (%) | 80.2 | #2 of 3 | Archive leaderboard | report |
| Semi-Supervised Text Classification | AG News (200 Labels) | 3 Layer MLP | Accuracy (%) | 77.5 | #3 of 3 | Archive leaderboard | report |
| Semi-Supervised Text Classification | Yahoo! Answers (800 Labels) | FlowGMM | Accuracy (%) | 57.9 | #1 of 3 | Archive leaderboard | report |
| Semi-Supervised Text Classification | Yahoo! Answers (800 Labels) | Pi Model | Accuracy (%) | 56.3 | #2 of 3 | Archive leaderboard | report |
| Semi-Supervised Text Classification | Yahoo! Answers (800 Labels) | 3 Layer MLP | Accuracy (%) | 55.7 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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