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Adversarial Robustness of Flow-Based Generative Models., , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 3795-3805. PMLR, (2020)Unrolling the Shutter: CNN to Correct Motion Distortions., , and . CVPR, page 2345-2353. IEEE Computer Society, (2017)Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation., , and . ICCV, page 6499-6507. IEEE, (2019)Learning to Balance Specificity and Invariance for In and Out of Domain Generalization., , and . ECCV (9), volume 12354 of Lecture Notes in Computer Science, page 301-318. Springer, (2020)Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models., , , , , , , , , and . ICCV, page 22873-22884. IEEE, (2023)Winning Lottery Tickets in Deep Generative Models., , and . AAAI, page 8038-8046. AAAI Press, (2021)A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes., , , and . CVPR, page 19065-19075. IEEE, (2022)Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation., , and . NeurIPS, (2020)JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation., , , , , , and . CVPR, page 6786-6795. IEEE, (2024)Generate to Adapt: Aligning Domains Using Generative Adversarial Networks., , , and . CVPR, page 8503-8512. Computer Vision Foundation / IEEE Computer Society, (2018)