no code implementations • 18 Aug 2020 • Divya Gaur, Joachim Folz, Andreas Dengel
The main purpose of this work is to determine if it is possible to train networks effectively when batch normalization is removed through adaption of the training process.
1 code implementation • 26 Mar 2020 • Shailza Jolly, Sebastian Palacio, Joachim Folz, Federico Raue, Joern Hees, Andreas Dengel
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets.
1 code implementation • CVPR 2018 • Sebastian Palacio, Joachim Folz, Jörn Hees, Federico Raue, Damian Borth, Andreas Dengel
To do this, an autoencoder (AE) was fine-tuned on gradients from a pre-trained classifier with fixed parameters.
Ranked #819 on Image Classification on ImageNet
no code implementations • 21 Mar 2018 • Joachim Folz, Sebastian Palacio, Joern Hees, Damian Borth, Andreas Dengel
We analyze their robustness against several white-box and gray-box scenarios on the large ImageNet dataset.
1 code implementation • 18 Sep 2017 • Benjamin Bischke, Patrick Helber, Joachim Folz, Damian Borth, Andreas Dengel
In this paper, we address the problem of preserving semantic segmentation boundaries in high resolution satellite imagery by introducing a new cascaded multi-task loss.
no code implementations • 25 Jul 2016 • Jörn Hees, Rouven Bauer, Joachim Folz, Damian Borth, Andreas Dengel
We show the scalability of the algorithm by running it against a SPARQL endpoint loaded with > 7. 9 billion triples.