1 code implementation • 11 Apr 2024 • Ming Li, Taojiannan Yang, Huafeng Kuang, Jie Wu, Zhaoning Wang, Xuefeng Xiao, Chen Chen
To this end, we propose ControlNet++, a novel approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency between generated images and conditional controls.
no code implementations • 30 Nov 2023 • Zhaoning Wang, Ming Li, Chen Chen
Nonetheless, achieving precise control over 3D generation continues to be an arduous task, as using text to control often leads to missing objects and imprecise locations.
no code implementations • CVPR 2023 • Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang, Fernando de la Torre
Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.
no code implementations • 23 Mar 2023 • Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang, Fernando de la Torre
Rather than relying on a carefully designed test set to assess ML models' failures, fairness, or robustness, this paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images to allow model diagnosis, interpretability, and robustness.
1 code implementation • 2 Feb 2022 • Xuefeng Du, Zhaoning Wang, Mu Cai, Yixuan Li
In this paper, we present VOS, a novel framework for OOD detection by adaptively synthesizing virtual outliers that can meaningfully regularize the model's decision boundary during training.
no code implementations • ICLR 2022 • Xuefeng Du, Zhaoning Wang, Mu Cai, Yixuan Li
In this paper, we present VOS, a novel framework for OOD detection by adaptively synthesizing virtual outliers that can meaningfully regularize the model's decision boundary during training.