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 applications 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. In this talk, I first introduce our efficient architecture based on filter-compositions and then, a novel approach to jointly learn the architecture and explicitly account for compression during the training process. Our results show that we can learn much more compact models and significantly reduce training and inference time.

Bio: Dr. Jose Alvarez is a senior research scientist at Toyota Research Institute. His main research interests are in developing robust and efficient deep learning algorithms for perception with focus on autonomous vehicles. Previously, he was a researcher at Data61 / CSIRO (formerly NICTA), a Postdoctoral researcher at the Courant Institute of Mathematical Science, New York University, and visiting scholar at University of Amsterdam and Group Research Electronics at Volkswagen. Dr. Alvarez graduated in 2012 and he was awarded the best Ph.D. Thesis award. Dr. Alvarez serves as associate editor for IEEE Trans. on Intelligent Transportation Systems.