Philip Haeusser
Philip Haeusser
PhD candidate, TUM (Technische Universität München)
Verified email at - Homepage
Cited by
Cited by
Flownet: Learning optical flow with convolutional networks
A Dosovitskiy, P Fischer, E Ilg, P Hausser, C Hazirbas, V Golkov, ...
Proceedings of the IEEE international conference on computer vision, 2758-2766, 2015
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N Mayer, E Ilg, P Hausser, P Fischer, D Cremers, A Dosovitskiy, T Brox
Proceedings of the IEEE conference on computer vision and pattern …, 2016
Associative Domain Adaptation
P Haeusser, T Frerix, A Mordvintsev, D Cremers
In IEEE International Conference on Computer Vision (ICCV), 2017
Purcell-enhanced single-photon emission from nitrogen-vacancy centers coupled to a tunable microcavity
H Kaupp, T Hümmer, M Mader, B Schlederer, J Benedikter, P Haeusser, ...
Physical Review Applied 6 (5), 054010, 2016
Learning by Association - A versatile semi-supervised training method for neural networks
P Häusser, A Mordvintsev, D Cremers
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Associative Deep Clustering: Training a Classification Network with no Labels
P Haeusser, J Plapp, V Golkov, E Aljalbout, D Cremers
Proc. of the German Conference on Pattern Recognition (GCPR), 2018
vd Smagt P, Cremers D, Brox T (2015) Flownet: Learning optical flow with convolutional networks
A Dosovitskiy, P Fischer, E Ilg, P Haeusser, C Hazirbas, V Golkov
Proc. of the IEEE International Conf. on Computer Vision (ICCV), 0
Better text understanding through image-to-text transfer
K Kurach, S Gelly, M Jastrzebski, P Haeusser, O Teytaud, D Vincent, ...
arXiv preprint arXiv:1705.08386, 2017
Semi-supervised training of neural networks
P Haeusser, A Mordvintsev
US Patent App. 16/461,287, 2020
Learning by Association
P Häusser
Technische Universität München, 2018
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