Learning by Association - A versatile semi-supervised training method for neural networks

1 code implementation • 3 Jun 2017 • Philip Häusser, Alexander Mordvintsev, Daniel Cremers

We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data.

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

3 code implementations • CVPR 2016 • Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox

By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.

Disparity Estimation Optical Flow Estimation +1

FlowNet: Learning Optical Flow with Convolutional Networks

18 code implementations • ICCV 2015 • Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox

Optical flow estimation has not been among the tasks where CNNs were successful.

Optical Flow Estimation