Search Results for author: Chaithanya Kumar Mummadi

Found 10 papers, 1 papers with code

Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation

no code implementations28 Jun 2021 Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach, Evgeny Levinkov, Thomas Brox, Jan Hendrik Metzen

This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.

Does enhanced shape bias improve neural network robustness to common corruptions?

no code implementations ICLR 2021 Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher, Julien Vitay, Volker Fischer, Jan Hendrik Metzen

We conclude that the data augmentation caused by style-variation accounts for the improved corruption robustness and increased shape bias is only a byproduct.

Data Augmentation

Group Pruning using a Bounded-Lp norm for Group Gating and Regularization

no code implementations9 Aug 2019 Chaithanya Kumar Mummadi, Tim Genewein, Dan Zhang, Thomas Brox, Volker Fischer

We achieve state-of-the-art pruning results for ResNet-50 with higher accuracy on ImageNet.

Defending Against Universal Perturbations With Shared Adversarial Training

no code implementations ICCV 2019 Chaithanya Kumar Mummadi, Thomas Brox, Jan Hendrik Metzen

Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.

Image Classification Semantic Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.