no code implementations • 15 Dec 2023 • Yitong Sun, Yao Huang, Xingxing Wei
As physical adversarial attacks become extensively applied in unearthing the potential risk of security-critical scenarios, especially in autonomous driving, their vulnerability to environmental changes has also been brought to light.
no code implementations • 13 Nov 2023 • Yitong Sun, Zijian Zhou, Cyriel Diels, Ali Asadipour
Despite the enhanced realism and immersion provided by VR headsets, users frequently encounter adverse effects such as digital eye strain (DES), dry eye, and potential long-term visual impairment due to excessive eye stimulation from VR displays and pressure from the mask.
1 code implementation • 27 Jul 2023 • Xingxing Wei, Yao Huang, Yitong Sun, Jie Yu
We also demonstrate the effectiveness of our approach in physical-world scenarios under various settings, including different angles, distances, postures, and scenes for both visible and infrared sensors.
1 code implementation • ICCV 2023 • Xingxing Wei, Yao Huang, Yitong Sun, Jie Yu
To show the potential risks under such scenes, we propose a unified adversarial patch to perform cross-modal physical attacks, i. e., fooling visible and infrared object detectors at the same time via a single patch.
no code implementations • 8 Jun 2022 • Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yitong Sun, Steven McDonagh
In this work, we propose to unify density ratio based methods under a novel framework that builds energy-based models and employs differing base distributions.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 29 Sep 2021 • Mingtian Zhang, Yitong Sun, Chen Zhang, Steven McDonagh
Flow-based models typically define a latent space with dimensionality identical to the observational space.
no code implementations • NeurIPS Workshop DLDE 2021 • Qi Sun, Hexin Dong, Zewei Chen, Weizhen Dian, Jiacheng Sun, Yitong Sun, Zhenguo Li, Bin Dong
Backpropagation algorithm is indispensable for training modern residual networks (ResNets) and usually tends to be time-consuming due to its inherent algorithmic lockings.
no code implementations • 1 Jan 2021 • Mingtian Zhang, Yitong Sun, Steven McDonagh, Chen Zhang
Flow-based generative models typically define a latent space with dimensionality identical to the observational space.
no code implementations • 1 Jan 2021 • Chen Zhang, Yitong Sun, Mingtian Zhang
However, in this paper, we point out that the method taken by GIN for informative latent variables identification is not theoretically supported and can be disproved by experiments.
no code implementations • 1 Jan 2021 • Xiaojiang Yang, Yitong Sun, Junchi Yan
In our experiments, we find that even the data is only augmented along a few latent variables, more latent variables can be identified, and adding a small noise in data space can stabilize this outcome.
no code implementations • 3 Sep 2020 • Qi Sun, Hexin Dong, Zewei Chen, Weizhen Dian, Jiacheng Sun, Yitong Sun, Zhenguo Li, Bin Dong
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which are time-consuming for deep ResNets.
no code implementations • 25 Nov 2019 • Xiaojiang Yang, Wendong Bi, Yitong Sun, Yu Cheng, Junchi Yan
Most existing works on disentangled representation learning are solely built upon an marginal independence assumption: all factors in disentangled representations should be statistically independent.
1 code implementation • 10 Oct 2018 • Yitong Sun, Anna Gilbert, Ambuj Tewari
We study the approximation properties of random ReLU features through their reproducing kernel Hilbert space (RKHS).
1 code implementation • NeurIPS 2018 • Yitong Sun, Anna Gilbert, Ambuj Tewari
We prove that, under low noise assumptions, the support vector machine with $N\ll m$ random features (RFSVM) can achieve the learning rate faster than $O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature map is used.