11 citations · 16 across the 2 of their papers we have counts for
7 papers
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
Bin Yang, Runsheng Guo, Ming Liang +2
We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the…
Open-Set Recognition Using Intra-Class Splitting
Patrick Schlachter, Yiwen Liao, Bin Yang
This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a…
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
Karim Armanious, Sherif Abdulatif, Fady Aziz +2
Radar is of vital importance in many fields, such as autonomous driving, safety and surveillance applications. However, it suffers from stringent constraints on its design parametr…
Deep One-Class Classification Using Intra-Class Splitting
Patrick Schlachter, Yiwen Liao, Bin Yang
This paper introduces a generic method which enables to use conventional deep neural networks as end-to-end one-class classifiers. The method is based on splitting given data from…
Active Learning for One-Class Classification Using Two One-Class Classifiers
Patrick Schlachter, Bin Yang
This paper introduces a novel, generic active learning method for one-class classification. Active learning methods play an important role to reduce the efforts of manual labeling…
One-Class Feature Learning Using Intra-Class Splitting
Patrick Schlachter, Yiwen Liao, Bin Yang
This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only sam…