| How much real data do we actually need: Analyzing object detection performance using synthetic and real data | Supervised training of deep models requires a large amount of annotated data to be available. However, data annotation is a tremendously exhausting and costly task to perform. One alternative is to use synthetic data. This paper provides a comprehensive study of the effects of replacing real data with synthetic data. | Jul 2019 | https://arxiv.org/abs/1907.07061 | International Conference on Machine Learning, ICML 2019 - AI for Autonomous Driving, Long Beach, California, USA |
| Deep Open Space Segmentation using Automotive Radar | This paper describes an AI approach for advanced deep segmentation from radar. This neural network is trained to identify open space in parking scenarios. Different deep models are evaluated with various radar input representations. This system achieves low memory usage and real-time processing speeds, and is thus very well suited for embedded deployment. | Mar 2020 | https://arxiv.org/pdf/2004.03449.pdf | International Conference on Microwaves for Intelligent Mobility, ICMIM 2020, Linz, Austria |
