36 citations · 38 across the 5 of their papers we have counts for
5 papers
Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions
Harshitha Machiraju, Michael H. Herzog, Pascal Frossard
Deep learning models often face challenges when handling real-world image corruptions. In response, researchers have developed image corruption datasets to evaluate the performance…
A comment on Guo et al. [arXiv:2206.11228]
Ben Lonnqvist, Harshitha Machiraju, Michael H. Herzog
In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially trained neural representations in deep networks may already be as robust as corresponding primate IT ne…
Empirical Advocacy of Bio-inspired Models for Robust Image Recognition
Harshitha Machiraju, Oh-Hyeon Choung, Michael H. Herzog +1
Deep convolutional neural networks (DCNNs) have revolutionized computer vision and are often advocated as good models of the human visual system. However, there are currently many…
Bio-inspired Robustness: A Review
Harshitha Machiraju, Oh-Hyeon Choung, Pascal Frossard +1
Deep convolutional neural networks (DCNNs) have revolutionized computer vision and are often advocated as good models of the human visual system. However, there are currently many…
One-shot learning and behavioral eligibility traces in sequential decision making
Marco Lehmann, He Xu, Vasiliki Liakoni +3
In many daily tasks we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessa…