176 citations · 255 across the 11 of their papers we have counts for
10 papers · 1 filter
BOSC: A toolbox for aerial imagery mapping
Ricard Durall, Laura Montilla, Esteban Durall
Accurate and efficient label of aerial images is essential for informed decision-making and resource allocation, whether in identifying crop types or delineating land-use patterns.…
Adversarial Examples are Misaligned in Diffusion Model Manifolds
Peter Lorenz, Ricard Durall, Janis Keuper
In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results. Nevert…
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
Peter Lorenz, Ricard Durall, Janis Keuper
Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for ma…
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces
Ricard Durall, Jireh Jam, Dominik Strassel +2
Facial image manipulation is a generation task where the output face is shifted towards an intended target direction in terms of facial attribute and styles. Recent works have achi…
Combining Transformer Generators with Convolutional Discriminators
Ricard Durall, Stanislav Frolov, Jörn Hees +4
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed wit…
Latent Space Conditioning on Generative Adversarial Networks
Ricard Durall, Kalun Ho, Franz-Josef Pfreundt +1
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this tr…
Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions
Ricard Durall, Margret Keuper, Janis Keuper
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or vid…
Local Facial Attribute Transfer through Inpainting
Ricard Durall, Franz-Josef Pfreundt, Janis Keuper
The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction,…
Semi Few-Shot Attribute Translation
Ricard Durall, Franz-Josef Pfreundt, Janis Keuper
Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications. However, most of existing approaches are based on deep learning and…
Object Segmentation using Pixel-wise Adversarial Loss
Ricard Durall, Franz-Josef Pfreundt, Ullrich Köthe +1
Recent deep learning based approaches have shown remarkable success on object segmentation tasks. However, there is still room for further improvement. Inspired by generative adver…