Zobrazeno 1 - 10
of 258
pro vyhledávání: '"Pfreundt, A."'
The widespread adoption of generative image models has highlighted the urgent need to detect artificial content, which is a crucial step in combating widespread manipulation and misinformation. Consequently, numerous detectors and associated datasets
Externí odkaz:
http://arxiv.org/abs/2403.17608
Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet. More recently, this focus has been
Externí odkaz:
http://arxiv.org/abs/2106.12303
Autor:
Durall, Ricard, Frolov, Stanislav, Hees, Jörn, Raue, Federico, Pfreundt, Franz-Josef, Dengel, Andreas, Keupe, Janis
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks. At the same time, image synthesis usi
Externí odkaz:
http://arxiv.org/abs/2105.10189
Despite the success of convolutional neural networks (CNNs) in many computer vision and image analysis tasks, they remain vulnerable against so-called adversarial attacks: Small, crafted perturbations in the input images can lead to false predictions
Externí odkaz:
http://arxiv.org/abs/2103.03000
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 Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These su
Externí odkaz:
http://arxiv.org/abs/2012.08803
Autor:
Bendle, Dominik, Boehm, Janko, Decker, Wolfram, Georgoudis, Alessandro, Pfreundt, Franz-Josef, Rahn, Mirko, Zhang, Yang
In this manuscript, which is to appear in the proceedings of the conference "MathemAmplitude 2019" in Padova, Italy, we provide an overview of the module intersection method for the the integration-by-parts (IBP) reduction of multi-loop Feynman integ
Externí odkaz:
http://arxiv.org/abs/2010.06895
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discri
Externí odkaz:
http://arxiv.org/abs/2007.03123
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, which is quantified by semantic attributes. Prominent example applicat
Externí odkaz:
http://arxiv.org/abs/2002.03040
Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities. Hence, several automatic selection algorithms have been introduced to overcome tedious manual (t
Externí odkaz:
http://arxiv.org/abs/2001.05726
Deep generative models have recently achieved impressive results for many real-world applications, successfully generating high-resolution and diverse samples from complex datasets. Due to this improvement, fake digital contents have proliferated gro
Externí odkaz:
http://arxiv.org/abs/1911.00686