604 citations · 704 across the 4 of their papers we have counts for
3 papers · 1 filter
Associative Domain Adaptation
Philip Haeusser, Thomas Frerix, Alexander Mordvintsev +1
We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain…
Learning by Association - A versatile semi-supervised training method for neural networks
Philip Häusser, Alexander Mordvintsev, Daniel Cremers
In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled dat…
FlowNet: Learning Optical Flow with Convolutional Networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg +6
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation ha…