91 citations · 210 across the 30 of their papers we have counts for
5 papers · 1 filter
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Sicheng Zhao, Xiangyu Yue, Shanghang Zhang +8
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively e…
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
Edward Kim, Divya Gopinath, Corina Pasareanu +1
Even as deep neural networks have become very effective for tasks in vision and perception, it remains difficult to explain and debug their behavior. In this paper, we present a pr…
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
Xiangyu Yue, Bichen Wu, Sanjit A. Seshia +2
3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point…
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
Sicheng Zhao, Bichen Wu, Joseph Gonzalez +2
Large-scale labeled training datasets have enabled deep neural networks to excel on a wide range of benchmark vision tasks. However, in many applications it is prohibitively expens…
Systematic Testing of Convolutional Neural Networks for Autonomous Driving
Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli +1
We present a framework to systematically analyze convolutional neural networks (CNNs) used in classification of cars in autonomous vehicles. Our analysis procedure comprises an ima…