StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization

Songhua Liu, Xin Jin, Xingyi Yang, Jingwen Ye, Xinchao Wang
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:31293-31311, 2024.

Abstract

Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain, making it a highly ambitious and challenging task. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data and thus increase robustness. Nevertheless, they have largely overlooked the underlying coherence between the augmented domains, which in turn leads to inferior results in real-world scenarios. In this paper, we propose a simple yet effective scheme, termed as StyDeSty, to explicitly account for the alignment of the source and pseudo domains in the process of data augmentation, enabling them to interact with each other in a self-consistent manner and further giving rise to a latent domain with strong generalization power. The heart of StyDeSty lies in the interaction between a stylization module for generating novel stylized samples using the source domain, and a destylization module for transferring stylized and source samples to a latent domain to learn content-invariant features. The stylization and destylization modules work adversarially and reinforce each other. During inference, the destylization module transforms the input sample with an arbitrary style shift to the latent domain, in which the downstream tasks are carried out. Specifically, the location of the destylization layer within the backbone network is determined by a dedicated neural architecture search (NAS) strategy. We evaluate StyDeSty on multiple benchmarks and demonstrate that it yields encouraging results, outperforming the state of the art by up to 13.44% on classification accuracy. Codes are available https://github.com/Huage001/StyDeSty.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-liu24ad, title = {{S}ty{D}e{S}ty: Min-Max Stylization and Destylization for Single Domain Generalization}, author = {Liu, Songhua and Jin, Xin and Yang, Xingyi and Ye, Jingwen and Wang, Xinchao}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {31293--31311}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/liu24ad/liu24ad.pdf}, url = {https://proceedings.mlr.press/v235/liu24ad.html}, abstract = {Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain, making it a highly ambitious and challenging task. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data and thus increase robustness. Nevertheless, they have largely overlooked the underlying coherence between the augmented domains, which in turn leads to inferior results in real-world scenarios. In this paper, we propose a simple yet effective scheme, termed as StyDeSty, to explicitly account for the alignment of the source and pseudo domains in the process of data augmentation, enabling them to interact with each other in a self-consistent manner and further giving rise to a latent domain with strong generalization power. The heart of StyDeSty lies in the interaction between a stylization module for generating novel stylized samples using the source domain, and a destylization module for transferring stylized and source samples to a latent domain to learn content-invariant features. The stylization and destylization modules work adversarially and reinforce each other. During inference, the destylization module transforms the input sample with an arbitrary style shift to the latent domain, in which the downstream tasks are carried out. Specifically, the location of the destylization layer within the backbone network is determined by a dedicated neural architecture search (NAS) strategy. We evaluate StyDeSty on multiple benchmarks and demonstrate that it yields encouraging results, outperforming the state of the art by up to 13.44% on classification accuracy. Codes are available https://github.com/Huage001/StyDeSty.} }
Endnote
%0 Conference Paper %T StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization %A Songhua Liu %A Xin Jin %A Xingyi Yang %A Jingwen Ye %A Xinchao Wang %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-liu24ad %I PMLR %P 31293--31311 %U https://proceedings.mlr.press/v235/liu24ad.html %V 235 %X Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain, making it a highly ambitious and challenging task. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data and thus increase robustness. Nevertheless, they have largely overlooked the underlying coherence between the augmented domains, which in turn leads to inferior results in real-world scenarios. In this paper, we propose a simple yet effective scheme, termed as StyDeSty, to explicitly account for the alignment of the source and pseudo domains in the process of data augmentation, enabling them to interact with each other in a self-consistent manner and further giving rise to a latent domain with strong generalization power. The heart of StyDeSty lies in the interaction between a stylization module for generating novel stylized samples using the source domain, and a destylization module for transferring stylized and source samples to a latent domain to learn content-invariant features. The stylization and destylization modules work adversarially and reinforce each other. During inference, the destylization module transforms the input sample with an arbitrary style shift to the latent domain, in which the downstream tasks are carried out. Specifically, the location of the destylization layer within the backbone network is determined by a dedicated neural architecture search (NAS) strategy. We evaluate StyDeSty on multiple benchmarks and demonstrate that it yields encouraging results, outperforming the state of the art by up to 13.44% on classification accuracy. Codes are available https://github.com/Huage001/StyDeSty.
APA
Liu, S., Jin, X., Yang, X., Ye, J. & Wang, X.. (2024). StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:31293-31311 Available from https://proceedings.mlr.press/v235/liu24ad.html.

Related Material