| Paper ID | SAM-13.6 |
| Paper Title |
TOWARDS ROBUST TRAINING OF MULTI-SENSOR DATA FUSION NETWORK AGAINST ADVERSARIAL EXAMPLES IN SEMANTIC SEGMENTATION |
| Authors |
Youngjoon Yu, Hong Joo Lee, Byeong Cheon Kim, Jung Uk Kim, Yong Man Ro, Korea Advanced Institute of Science and Technology (KAIST), South Korea |
| Session | SAM-13: Multi-Channel Data Fusion and Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Sensor Array and Multichannel Signal Processing: [SAM-LRNM] Learning models and methods for multi-sensor systems |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The success of multi-sensor data fusions in deep learning appears to be attributed to the use of complementary information among multiple sensor datasets. Compared to their predictive performance, relatively less attention has been devoted to the adversarial robustness of multi-sensor data fusion models. To achieve adversarial robust multi-sensor data fusion networks, we propose here a novel robust training scheme called Multi-Sensor Cumulative Learning (MSCL). The motivation behind the MSCL method is based on the way human beings learn new skills. The MSCL allows the multi-sensor fusion network to learn robust features from individual sensors, and then learn complex joint features from multiple sensors just as people learn to walk before they run. The step wise framework of MSCL enables the network to incorporate pre-trained knowledge of robustness with new joint information from multiple sensors. Extensive experimental evidence validated that the MSCL outperforms other multi-sensor fusion training in defending against adversarial examples. |
