Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained
with noisy labels for a long enough time. To overcome this problem, we present a simple and …

While deep learning is remarkably successful on perceptual tasks, it was also shown to be
vulnerable to adversarial perturbations of the input. These perturbations denote noise added …

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

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 demonstrate …

Convolutional neural networks (CNNs) learn to extract representations of complex features,
such as object shapes and textures to solve image recognition tasks. Recent work indicates …

This article focuses on the use of data gloves for human-computer interaction concepts,
where external sensors cannot always fully observe the user’s hand. A good concept hereby …

Deep neural network (DNN) based salient object detection in images based on high-quality
labels is expensive. Alternative unsupervised approaches rely on careful selection of …

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 …

Neural architectures based on attention such as vision transformers are revolutionizing image
recognition. Their main benefit is that attention allows reasoning about all parts of a scene …

Data gloves have numerous applications, including enabling novel human-computer
interaction and automated recognition of large sets of gestures, such as those used for sign …