1.5k citations · 4.4k across the 17 of their papers we have counts for
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FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia +3
The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined…
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig +4
In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstraine…
Learning to Act by Predicting the Future
Alexey Dosovitskiy, Vladlen Koltun
We present an approach to sensorimotor control in immersive environments. Our approach utilizes a high-dimensional sensory stream and a lower-dimensional measurement stream. The co…
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski +2
Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner working…
Generating Images with Perceptual Similarity Metrics based on Deep Networks
Alexey Dosovitskiy, Thomas Brox
Image-generating machine learning models are typically trained with loss functions based on distance in the image space. This often leads to over-smoothed results. We propose a cla…