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

Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:1479-1490, 2024.

Abstract

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 [github.com/gudovskiy/contextflow](https://github.com/gudovskiy/contextflow).

Cite this Paper

BibTeX
@InProceedings{pmlr-v244-gudovskiy24a, title = {ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding}, author = {Gudovskiy, Denis and Okuno, Tomoyuki and Nakata, Yohei}, booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence}, pages = {1479--1490}, year = {2024}, editor = {Kiyavash, Negar and Mooij, Joris M.}, volume = {244}, series = {Proceedings of Machine Learning Research}, month = {15--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/gudovskiy24a/gudovskiy24a.pdf}, url = {https://proceedings.mlr.press/v244/gudovskiy24a.html}, abstract = {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 [github.com/gudovskiy/contextflow](https://github.com/gudovskiy/contextflow).} }
Endnote
%0 Conference Paper %T ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding %A Denis Gudovskiy %A Tomoyuki Okuno %A Yohei Nakata %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-gudovskiy24a %I PMLR %P 1479--1490 %U https://proceedings.mlr.press/v244/gudovskiy24a.html %V 244 %X 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 [github.com/gudovskiy/contextflow](https://github.com/gudovskiy/contextflow).
APA
Gudovskiy, D., Okuno, T. & Nakata, Y.. (2024). ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:1479-1490 Available from https://proceedings.mlr.press/v244/gudovskiy24a.html.

Related Material