This paper provides an overview of current approaches for solving inverse problems in
imaging using variational methods and machine learning. A special focus lies on point …

Pixel-wise predictions are required in a wide variety of tasks such as image restoration,
image segmentation, or disparity estimation. Common models involve several stages of data …

Image restoration networks are usually comprised of an encoder and a decoder, responsible
for aggregating image content from noisy, distorted data and to restore clean, undistorted …

State-of-the-art models for pixel-wise prediction tasks such as image restoration, image
segmentation, or disparity estimation, involve several stages of data resampling, in which …

Not all learnable parameters (eg, weights) contribute equally to a neural network's decision
function. In fact, entire layers' parameters can sometimes be reset to random values with little …

Deep neural networks are susceptible to adversarial attacks and common corruptions, which
undermine their robustness. In order to enhance model resilience against such challenges …

The well-known vulnerability of Neural Networks to adversarial attacks is concerning, more
so with the increasing reliance on them for real-world applications like autonomous driving …