12 citations · 21 across the 5 of their papers we have counts for
6 papers
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
Shu Hu, George H. Chen
We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a use…
Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces
Hui Guo, Shu Hu, Xin Wang +2
AI-synthesized faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social i…
PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems
Shu Hu, Chun-Hao Liu, Jayanta Dutta +3
Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled. To propagate information from the labeled obje…
TML-AP: Adversarial Attacks to Top- Multi-Label Learning
Shu Hu, Lipeng Ke, Xin Wang +1
Top- multi-label learning, which returns the top- predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search…
Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights
Shu Hu, Yuezun Li, Siwei Lyu
Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. In this work,…
Learning by Minimizing the Sum of Ranked Range
Shu Hu, Yiming Ying, Xin Wang +1
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual l…