598 citations · 2.9k across the 89 of their papers we have counts for
16 papers · 1 filter
Identifying Systematic Errors in Object Detectors with the SCROD Pipeline
Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen
The identification and removal of systematic errors in object detectors can be a prerequisite for their deployment in safety-critical applications like automated driving and roboti…
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models
Francesco Croce, Naman D Singh, Matthias Hein
Adversarial robustness has been studied extensively in image classification, especially for the -threat model, but significantly less so for related tasks such as obje…
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
Naman D Singh, Francesco Croce, Matthias Hein
While adversarial training has been extensively studied for ResNet architectures and low resolution datasets like CIFAR, much less is known for ImageNet. Given the recent debate ab…
Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet
Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko +1
Benchmark performance of deep learning classifiers alone is not a reliable predictor for the performance of a deployed model. In particular, if the image classifier has picked up s…
Diffusion Visual Counterfactual Explanations
Maximilian Augustin, Valentyn Boreiko, Francesco Croce +1
Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are 'small' but 'realistic' semantic changes of the image c…
On the interplay of adversarial robustness and architecture components: patches, convolution and attention
Francesco Croce, Matthias Hein
In recent years novel architecture components for image classification have been developed, starting with attention and patches used in transformers. While prior works have analyze…