1 code implementation • 3 May 2024 • Junchen Liu, WenBo Hu, Zhuo Yang, Jianteng Chen, Guoliang Wang, Xiaoxue Chen, Yantong Cai, Huan-ang Gao, Hao Zhao
Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge to effectively and efficiently characterize anisotropic areas induced by the cone-casting procedure.
1 code implementation • 20 Apr 2023 • Yongming Yang, Shuwei Shao, Tao Yang, Peng Wang, Zhuo Yang, Chengdong Wu, Hao liu
To address this issue, we introduce a gradient loss to penalize edge fluctuations ambiguous around stepped edge structures and a normal loss to explicitly express the sensitivity to frequently small structures, and propose a geometric consistency loss to spreads the spatial information across the sample grids to constrain the global geometric anatomy structures.
1 code implementation • 29 Jun 2021 • Zhuo Yang, Yufei Han, Xiangliang Zhang
We unveil how the transferability level of the attack determines the attackability of the classifier via establishing an information-theoretic analysis of the adversarial risk.
no code implementations • 17 Dec 2020 • Zhuo Yang, Yufei Han, Xiangliang Zhang
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic.
no code implementations • 10 Aug 2020 • Xiaomei Bai, Mengyang Wang, Ivan Lee, Zhuo Yang, Xiangjie Kong, Feng Xia
The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation.
no code implementations • 17 Nov 2019 • Zhuo Yang, Yufei Han, Guoxian Yu, Qiang Yang, Xiangliang Zhang
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.