Invited Talk: Efficient Deep Networks for Real-Time Classification in Embedded Platforms (Jose Alvarez, NICTA, Australia)

Abstract: Convolutional neural networks have achieved impressive success in many tasks in computer vision such as image classification, object detection / recognition or semantic segmentation. While these networks have proven effective in all these applications, they come at a high memory and computational cost, thus not feasible for embedded platforms where power and computational resources are limited. In addition, the process to train the network reduces productivity as it not only requires large computer servers but also takes a significant amount of time (several weeks) with the additional cost of engineering the architecture. Recent works have shown there is significant redundancy in the parameters of deep architectures and therefore, could be replaced by more compact architectures. In this talk, I first introduce our efficient architecture based on filter-compositions and then, a novel approach to automatically determining the optimal number of neurons per layer in the architecture during the training process. As a result, we are able to deliver competitive accuracy and achieve up to 230fps in an embedded platform (Jetson TX-1). Moreover, these networks enable rapid prototyping as their entire training process only requires a few days.

Bio: Dr. Jose M. Alvarez is a computer vision researcher at Data61 at CSIRO (formerly NICTA) working on efficient methods for large-scale dynamic scene understanding and deep learning. Dr. Alvarez graduated with his Ph.D. from Autonomous University of Barcelona (UAB) in October 2010. During his Ph.D., his research was focused on developing robust road detection algorithms for everyday driving tasks under real-world conditions. Dr. Alvarez visited the ISLA group at the University of Amsterdam (in 2008 and 2009), and the Group Research Electronics at Volkswagen (in 2010). Dr. Alvarez was awarded the best Ph.D. Thesis award in 2010 from the Autonomous University of Barcelona. Subsequently, Dr. Alvarez worked as a postdoctoral researcher at the Courant Institute of Mathematical Science, New York University. In 2012, Dr. Alvarez moved to the computer vision group at NICTA, Australia. Since 2014, Dr. Alvarez serves as associate editor for IEEE Transactions on Intelligent Transportation Systems.