Efficient deep networks for real-world interaction

Deep neural networks are essential in applications such as image categorization, natural language processing, autonomous driving, home automation, and robotics. Most of these applications require instantaneous processing of data and decision making. In general existing neural networks are computationally expensive, and hence they fail to perform in real-time. Models performing semantic segmentation are being extensively used in self-driving vehicles. Autonomous vehicles not only need segmented output, but also control system capable of processing segmented output and deciding actuator outputs such as speed and direction.

In this thesis we propose efficient neural network architectures with fewer operations and parameters as compared to current state-of-the-art algorithms. Our work mainly focuses on designing deep neural network architectures for semantic segmentation. First, we introduce few network modules and concepts which help in reducing model complexity. Later on, we show that in terms of accuracy our proposed networks perform better or at least at par with state-of-the-art neural networks. Apart from that, we also compare our networks' performance on edge devices such as Nvidia TX1. Lastly, we present a control system capable of predicting steering angle and speed of a vehicle based on the neural network output.