Search Results for author:
Found 9 papers, 6 papers with code
Autonomously and Simultaneously Refining Deep Neural Network Parameters by Generative Adversarial Networks
Our proposed approach can be used to autonomously refine the parameters, and improve the accuracy of different deep neural network architectures.
Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm
The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks.
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they maintain their effectiveness even against other models.
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.
Autonomous Human Activity Classification from Ego-vision Camera and Accelerometer Data
For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is.
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they remain adversarial even against other models.
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps.
Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation
It is worth to note that our proposed RAA convolution is lightweight and compatible to be integrated into any CNN architecture used for detection from a BEV.
