Philip HäusserAlumniTechnische Universität München
Department of Computer Science
I'm a third-year PhD student in computer science at TUM, doing research in computer vision and machine learning (colloquially known as “artificial intelligence”) advised by Prof. Daniel Cremers.
I hold a Master's degree in physics from the University of California, Santa Cruz (USA) and a Bachelor's degree in physics from the LMU Munich where I was working at the cavity quantum optics group headed by Prof. T.W. Haensch.
When I'm not in the lab you might find me playing squash or volleyball or you might encounter one of my TV productions.
I will be on leave from May 2017 – September 2017 for an internship at Google..
Deep learning projects
- Optical flow estimation (coop with Freiburg; ICCV paper 2015)
- Scene flow estimation (coop with Freiburg; CVPR paper 2016)
- Facial expression recognition (student project)
- Mathematical handwriting recognition (Bachelor thesis)
- Video frame prediction (Master's thesis)
- Construction zone recognition for self-driving cars (Master's thesis)
- Semi-supervised training “learning by association” (Google internship 2016; CVPR paper 2017)
- Domain adaptation with neural networks
Summer Term 2015
- Master-Praktikum/Lab Course Deep learning for the real world [10 ECTS]
Summer Term 2016
Winter Term 2016/17
Summer Term 2018
|Conference and Workshop Papers|
|Associative Deep Clustering - Training a Classification Network with no Labels , In Proc. of the German Conference on Pattern Recognition (GCPR), 2018. [bib] [pdf]|
|Associative Domain Adaptation , In IEEE International Conference on Computer Vision (ICCV), 2017.([code] [PDF from CVF]) [bib] [pdf]|
|Better Text Understanding Through Image-To-Text Transfer , In arxiv:1705.08386, 2017. [bib] [pdf]|
|Learning by Association - A versatile semi-supervised training method for neural networks , In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.([code] [PDF from CVF]) [bib] [pdf]|
|A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation , In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016.(arXiv:1512.02134) [bib] [pdf]|
|FlowNet: Learning Optical Flow with Convolutional Networks , In IEEE International Conference on Computer Vision (ICCV), 2015.([video],[code]) [bib] [pdf] [doi]|
- Deep Learning and Convolutional Networks, July 9th 2015, University of Augsburg
- Kuenstliche Intelligenz und Digitalisierung, April 8th, 2017, Akademie fuer politische Bildung, Tutzing