176 citations · 250 across the 54 of their papers we have counts for
11 papers · 1 filter
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context
Shashank Agnihotri, Julia Grabinski, Margret Keuper
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…
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix
Shashank Agnihotri, Kanchana Vaishnavi Gandikota, Julia Grabinski +2
Following their success in visual recognition tasks, Vision Transformers(ViTs) are being increasingly employed for image restoration. As a few recent works claim that ViTs for imag…
An Evaluation of Zero-Cost Proxies -- from Neural Architecture Performance to Model Robustness
Jovita Lukasik, Michael Moeller, Margret Keuper
Zero-cost proxies are nowadays frequently studied and used to search for neural architectures. They show an impressive ability to predict the performance of architectures by making…
As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain
Julia Grabinski, Janis Keuper, Margret Keuper
Recent work in neural networks for image classification has seen a strong tendency towards increasing the spatial context. Whether achieved through large convolution kernels or sel…
Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling
Julia Grabinski, Steffen Jung, Janis Keuper +1
Convolutional Neural Networks (CNNs) are successful in various computer vision tasks. From an image and signal processing point of view, this success is counter-intuitive, as the i…
Neural Architecture Design and Robustness: A Dataset
Steffen Jung, Jovita Lukasik, Margret Keuper
Deep learning models have proven to be successful in a wide range of machine learning tasks. Yet, they are often highly sensitive to perturbations on the input data which can lead…
Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models
Steffen Jung, Jan Christian Schwedhelm, Claudia Schillings +1
In recent years, optimization in the learned latent space of deep generative models has been successfully applied to black-box optimization problems such as drug design, image gene…
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
Hendrik Sommerhoff, Shashank Agnihotri, Mohamed Saleh +3
The success of deep learning is frequently described as the ability to train all parameters of a network on a specific application in an end-to-end fashion. Yet, several design cho…
An Extended Study of Human-like Behavior under Adversarial Training
Paul Gavrikov, Janis Keuper, Margret Keuper
Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by…
FullFormer: Generating Shapes Inside Shapes
Tejaswini Medi, Jawad Tayyub, Muhammad Sarmad +2
Implicit generative models have been widely employed to model 3D data and have recently proven to be successful in encoding and generating high-quality 3D shapes. This work builds…
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
Shashank Agnihotri, Steffen Jung, Margret Keuper
While neural networks allow highly accurate predictions in many tasks, their lack of robustness towards even slight input perturbations often hampers their deployment. Adversarial…