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Found 10 papers, 2 papers with code
A Survey on GAN Acceleration Using Memory Compression Technique
Thus, the architecture and numeric precision of the network affect the quality and speed of the solution.
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents
Our model's learned representation leads to better and more semantically meaningful coverage of the trajectory distribution.
Motion Forecasting with Unlikelihood Training
We propose a new objective, unlikelihood training, which forces generated trajectories that conflicts with contextual information to be assigned a lower probability by our model.
The Case for Learning Application Behavior to Improve Hardware Energy Efficiency
During the execution of an unseen application, the model uses the learned knowledge to reconfigure hardware resources in order to maximize energy efficiency.
Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation
Evaluation is performed on unseen real LiDAR frames from KITTI dataset, with different amounts of simulated data augmentation using the two proposed approaches, showing improvement of 6% mAP for the object detection task, in favor of the augmenting LiDAR point clouds adapted with the proposed neural sensor models over the raw simulated LiDAR.
End-to-end sensor modeling for LiDAR Point Cloud
The major problem with such approaches is that the amount of training data required for generalizing a machine learning model is big, and on the other hand LiDAR data annotation is very costly compared to other car sensors.
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
Simulators are often used for data augmentation, which requires realistic sensor models that are hard to formulate and model in closed forms.
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding environment, while the task of 3D object bounding box detection in real time remains a strong algorithmic challenge.
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving
Our experiments show that the proposed method outperforms state of the art methods that utilize motion cue only with 21. 5% in mAP on KITTI MOD.
