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ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding

2 Jun 2024arXiv:2406.00578archive 2025-07-28

Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata

Normalizing flow-based generative models have been widely used in applications where the exact density estimation is of major importance. Recent research proposes numerous methods to improve their expressivity. However, conditioning on a context is largely overlooked area in the bijective flow research. Conventional conditioning with the vector concatenation is limited to only a few flow types. More importantly, this approach cannot support a practical setup where a set of context-conditioned (specialist) models are trained with the fixed pretrained general-knowledge (generalist) model. We propose ContextFlow++ approach to overcome these limitations using an additive conditioning with explicit generalist-specialist knowledge decoupling. Furthermore, we support discrete contexts by the proposed mixed-variable architecture with context encoders. Particularly, our context encoder for discrete variables is a surjective flow from which the context-conditioned continuous variables are sampled. Our experiments on rotated MNIST-R, corrupted CIFAR-10C, real-world ATM predictive maintenance and SMAP unsupervised anomaly detection benchmarks show that the proposed ContextFlow++ offers faster stable training and achieves higher performance metrics. Our code is publicly available at https://github.com/gudovskiy/contextflow.

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Tasks

Anomaly DetectionDensity EstimationGeneral KnowledgeRotated MNISTTime Series Anomaly DetectionUnsupervised Anomaly Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unsupervised Anomaly Detection SMAP ContextFlow++ (Glow-based) AUC 98.66 #2 of 9 Archive leaderboard report
Unsupervised Anomaly Detection SMAP ContextFlow++ (Glow-based) F1 93.62 #2 of 9 Archive leaderboard report
Unsupervised Anomaly Detection SMAP ContextFlow++ (Glow-based) Precision 88.64 #2 of 9 Archive leaderboard report
Unsupervised Anomaly Detection SMAP ContextFlow++ (Glow-based) Recall 99.19 #2 of 9 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.

Methods

Activation NormalizationAffine CouplingGLOWInvertible 1x1 ConvolutionNormalizing FlowsSET

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