2.6k citations · 4.6k across the 52 of their papers we have counts for
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Multimodal Deep Learning for Robust RGB-D Object Recognition
Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello +2
Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs…
Learning to Generate Chairs, Tables and Cars with Convolutional Networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Maxim Tatarchenko +1
We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color. We train the networks on rendered 3D m…
Unsupervised feature learning by augmenting single images
Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox
When deep learning is applied to visual object recognition, data augmentation is often used to generate additional training data without extra labeling cost. It helps to reduce ove…