604 citations · 1k across the 30 of their papers we have counts for
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Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach +3
Deep neural networks often exhibit poor performance on data that is unlikely under the train-time data distribution, for instance data affected by corruptions. Previous works demon…
Group Pruning using a Bounded-Lp norm for Group Gating and Regularization
Chaithanya Kumar Mummadi, Tim Genewein, Dan Zhang +2
Deep neural networks achieve state-of-the-art results on several tasks while increasing in complexity. It has been shown that neural networks can be pruned during training by impos…
Adversarial Examples for Semantic Image Segmentation
Volker Fischer, Mummadi Chaithanya Kumar, Jan Hendrik Metzen +1
Machine learning methods in general and Deep Neural Networks in particular have shown to be vulnerable to adversarial perturbations. So far this phenomenon has mainly been studied…