• Publications
  • Influence
Striving for Simplicity: The All Convolutional Net
We propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR100, ImageNet). Expand
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A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
  • N. Mayer, Eddy Ilg, +4 authors T. Brox
  • Computer Science, Mathematics
  • IEEE Conference on Computer Vision and Pattern…
  • 7 December 2015
We propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks for scene flow estimation. Expand
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FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
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 by traditionalExpand
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CARLA: An Open Urban Driving Simulator
We introduce CARLA, an open-source simulator for autonomous driving research. Expand
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Generating Images with Perceptual Similarity Metrics based on Deep Networks
We propose a class of loss functions, which we call deep perceptual similarity metrics (DeePSiM), that mitigate this problem. Expand
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DeMoN: Depth and Motion Network for Learning Monocular Stereo
We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstrained image pairs. Expand
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On Evaluation of Embodied Navigation Agents
We convened a working group to study empirical methodology in navigation research. Expand
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Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Expand
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Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
We show that the feature representation learned by the Exemplar-CNN performs well on two very different tasks: object classification and descriptor matching. Expand
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Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
We introduce a novel training procedure for training a convolutional neural network using only unlabeled data that does not require any labeled data. Expand
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