Search Results for author:
Found 3 papers, 1 papers with code
Learning Neural Light Transport
In recent years, deep generative models have gained significance due to their ability to synthesize natural-looking images with applications ranging from virtual reality to data augmentation for training computer vision models.
Automated Deep Photo Style Transfer
This is achieved by introducing a constraint that prevents distortions in the content image and by applying the style transfer independently for semantically different parts of the images.
Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning
This paper introduces therefore a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently towards the optimal solution.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
