10 citations · 13 across the 9 of their papers we have counts for
7 papers · 1 filter
Image Background Serves as Good Proxy for Out-of-distribution Data
Sen Pei
Out-of-distribution (OOD) detection empowers the model trained on the closed image set to identify unknown data in the open world. Though many prior techniques have yielded conside…
Free Lunch for Generating Effective Outlier Supervision
Sen Pei, Jiaxi Sun, Richard Yi Da Xu +3
When deployed in practical applications, computer vision systems will encounter numerous unexpected images (\emph{i.e.}, out-of-distribution data). Due to the potentially raised sa…
Exploring Domain Incremental Video Highlights Detection with the LiveFood Benchmark
Sen Pei, Shixiong Xu, Xiaojie Jin
Video highlights detection (VHD) is an active research field in computer vision, aiming to locate the most user-appealing clips given raw video inputs. However, most VHD methods ar…
Domain Decorrelation with Potential Energy Ranking
Sen Pei, Jiaxi Sun, Richard Yi Da Xu +2
Machine learning systems, especially the methods based on deep learning, enjoy great success in modern computer vision tasks under experimental settings. Generally, these classic d…
Gradient Concealment: Free Lunch for Defending Adversarial Attacks
Sen Pei, Jiaxi Sun, Xiaopeng Zhang +1
Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks. However, even the \emph{state-of-the-art} deep learning based classifiers are…
Out-of-distribution Detection with Boundary Aware Learning
Sen Pei, Xin Zhang, Bin Fan +1
There is an increasing need to determine whether inputs are out-of-distribution (\emph{OOD}) for safely deploying machine learning models in the open world scenario. Typical neural…
Alleviating Mode Collapse in GAN via Diversity Penalty Module
Sen Pei, Richard Yi Da Xu, Shiming Xiang +1
The vanilla GAN (Goodfellow et al. 2014) suffers from mode collapse deeply, which usually manifests as that the images generated by generators tend to have a high similarity amongs…