De-hyping Neural Networks
Prof. Bodo Rosenhahn
Leibniz University Hannover (Germany)
Time & Place
Fri, 29 Mar 2019 14:00:00 NZDT in Jack Erskine 340
Computer Vision is revolved from recent developments in machine learning, especially deep learning. Convolutional neural networks, different topologies and strategies, e.g. based on drop-outs, skip connections, autoencoders, adversarial networks, together with huge amount of training data or reinforcement learning paradigms allow for amazing tools. Autonomous driving, recommender systems, medical data analysis, industry 4.0, games or even arts are famous fields for applications of machine learning.
In this talk I will give an overview of our research at the institute for information processing in Hanover. Starting with an overview on machine learning and basic paradigms, I will switch over to current challenges and research with a glimpse on our applications in industrial projects. I will cover several applications from object detection, semantic segmentation, autoencoder, human pose estimation, autonomous navigation and medical data analysis. Additionally I will reflect pros and cons of neural networks and I will share some basic insights we have gathered over the last years.
Bodo Rosenhahn studied Computer Science (minor subject Medicine) at the University of Kiel. He received the Dipl.-Inf. and Dr.-Ing. degrees from the University of Kiel in 1999 and 2003, respectively. From 10/2003 till 10/2005, he worked as post doc at the University of Auckland (New Zealand), funded with a scholarship from the German Research Foundation (DFG). In 11/2005-08/2008 he worked as senior researcher at the Max-Planck Insitute for Informatics in Saarbruecken. Since 09/2008 he is Full Professor at the Leibniz University Hannover, heading a group on automated image interpretation.