37 citations · 150 across the 18 of their papers we have counts for
4 papers · 1 filter
A Simple Strategy to Provable Invariance via Orbit Mapping
Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner +2
Many applications require robustness, or ideally invariance, of neural networks to certain transformations of input data. Most commonly, this requirement is addressed by training d…
Training or Architecture? How to Incorporate Invariance in Neural Networks
Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner +2
Many applications require the robustness, or ideally the invariance, of a neural network to certain transformations of input data. Most commonly, this requirement is addressed by e…
Fast Convex Relaxations using Graph Discretizations
Jonas Geiping, Fjedor Gaede, Hartmut Bauermeister +1
Matching and partitioning problems are fundamentals of computer vision applications with examples in multilabel segmentation, stereo estimation and optical-flow computation. These…
Inverting Gradients -- How easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge +1
The idea of federated learning is to collaboratively train a neural network on a server. Each user receives the current weights of the network and in turns sends parameter updates…