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Found 3 papers, 1 papers with code
On Detecting Adversarial Perturbations
In this work, we propose to augment deep neural networks with a small "detector" subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations.
Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks
The proposed multi-scale KPPillarsBEV architecture outperforms the baseline by 5. 37% and the previous state of the art by 2. 88% in Car AP4. 0 (average precision for a matching threshold of 4 meters) on the nuScenes validation set.
Exploiting Sparsity in Automotive Radar Object Detection Networks
This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources.
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