4 accepted papers
In the process of training Neural Networks, pre-training is an unsupervised training process that uses automatically generated labels for real end-goal task inputs. It usually precedes a supervised training stage, can improve neural network performance, and can reduce training loss. In this work, we…
We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image. This work marks a first attempt to address this task with on-board sensing without assuming a known constant lane width or relying on pre-mapped environments. Our network architecture, 3D-LaneNe…
Attaining accurate estimation of a signal time-of-arrival (TOA) in dense multipath channels is very challenging. This problem was traditionally solved with signal processing techniques. In this paper, a novel deep convolutional neural networks (DCNN) TOA estimator is developed. The DCNN was trained…
Recent breakthroughs in computer vision make use of large deep neural networks, utilizing the substantial speedup offered by GPUs. For applications running on limited hardware, however, high precision real-time processing can still be a challenge. One approach to solving this problem is training ne…