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Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox +1
Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small…
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…
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg +2
Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training…
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
Philipp Fischer, Alexey Dosovitskiy, Thomas Brox
Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that th…
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
Peter Ochs, Yunjin Chen, Thomas Brox +1
In this paper we study an algorithm for solving a minimization problem composed of a differentiable (possibly non-convex) and a convex (possibly non-differentiable) function. The a…