Search Results for author: Yujia Huang

Found 7 papers, 2 papers with code

Diffusion Models for Adversarial Purification

no code implementations16 May 2022 Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar

Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model.

Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds

1 code implementation NeurIPS 2021 Yujia Huang, huan zhang, Yuanyuan Shi, J Zico Kolter, Anima Anandkumar

Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by computing a global bound on its Lipschitz constant.

Neural Networks with Recurrent Generative Feedback

1 code implementation NeurIPS 2020 Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Y. Tsao, Anima Anandkumar

This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment.

Adversarial Robustness

Out-of-Distribution Detection Using Neural Rendering Generative Models

no code implementations10 Jul 2019 Yujia Huang, Sihui Dai, Tan Nguyen, Richard G. Baraniuk, Anima Anandkumar

Our results show that when trained on CIFAR-10, lower likelihood (of latent variables) is assigned to SVHN images.

Neural Rendering OOD Detection +1

Semantic-driven Generation of Hyperlapse from $360^\circ$ Video

no code implementations31 Mar 2017 Wei-Sheng Lai, Yujia Huang, Neel Joshi, Chris Buehler, Ming-Hsuan Yang, Sing Bing Kang

We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience.

Video Stabilization

Pose Invariant Embedding for Deep Person Re-identification

no code implementations26 Jan 2017 Liang Zheng, Yujia Huang, Huchuan Lu, Yi Yang

Second, to reduce the impact of pose estimation errors and information loss during PoseBox construction, we design a PoseBox fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input.

Person Re-Identification Pose Estimation

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