Are Saddles Good Enough for Deep Learning?

Recent years have seen a growing interest in understanding deep neural networks from an optimization perspective. It is understood now that converging to low-cost local minima is sufficient for such models to become effective in practice. However, in this work, we propose a new hypothesis based on recent theoretical findings and empirical studies that deep neural network models actually converge to saddle points with high degeneracy. Our findings from this work are new, and can have a significant impact on the development of gradient descent based methods for training deep networks. We validated our hypotheses using an extensive experimental evaluation on standard datasets such as MNIST and CIFAR-10, and also showed that recent efforts that attempt to escape saddles finally converge to saddles with high degeneracy, which we define as `good saddles'. We also verified the famous Wigner's Semicircle Law in our experimental results

Segmentación semántica para imágenes fisheye basada en CNN

Este trabajo fin de máster (TFM) está centrado en el desarrollo de un sistema de percepción basado en cámaras de campo de visión ultra-amplio de tipo fisheye para un prototipo de vehículo autónomo eléctrico. El método escogido para afrontar la comprensión del entorno a partir del sistema es el uso de segmentación semántica mediante redes neuronales convolucionales (CNN) dado que permite cubrir la mayoría de necesidades de un vehículo autónomo de manera unificada. Para ello se desarrollan nuevas arquitecturas de red, mecanismos de data-augmentation y datasets sintéticos específicos para abordar el problema generado por la fuerte distorsión.This work aims to develope a new simplified perceptive system for a full autonomous electric vehicle prototype based on ultra-wide field of view cameras. Semantic segmentation is chosen as the way to face scene-understanding as it satisfies most of autonomous vehicle needs in an unified way. For this purpose, specific elements to deal with distortion are designed, such as network architectures, data-augmentation techniques or synthetic fisheye datasets.Máster Universitario en Ingeniería de Telecomunicación (M125