Ongoing projects in our group
Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have however relied upon massive amounts of human annotated training data. This time-consuming process has begun impeding the progress of these deep learning efforts. By training machine learning algorithms on a rich virtual world, we can illustrate that real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning’s application to sensor based classification problems like those that appear in self-driving cars.