1 code implementation • 29 Jan 2024 • Felix Friedrich, Katharina Hämmerl, Patrick Schramowski, Jindrich Libovicky, Kristian Kersting, Alexander Fraser
Text-to-image generation models have recently achieved astonishing results in image quality, flexibility, and text alignment and are consequently employed in a fast-growing number of applications.
no code implementations • 28 Nov 2023 • Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinário Passos
Second, our methodology supports multiple simultaneous edits and is architecture-agnostic.
no code implementations • 15 Sep 2023 • Wolfgang Stammer, Felix Friedrich, David Steinmann, Hikaru Shindo, Kristian Kersting
In contrast to current AI research that mainly treats explanations as a means for model inspection, a somewhat neglected finding from human psychology is the benefit of self-explaining in an agents' learning process.
no code implementations • 25 Aug 2023 • David Steinmann, Wolfgang Stammer, Felix Friedrich, Kristian Kersting
Specifically, a CB2M learns to generalize interventions to appropriate novel situations via a two-fold memory with which it can learn to detect mistakes and to reapply previous interventions.
no code implementations • 28 May 2023 • Manuel Brack, Felix Friedrich, Patrick Schramowski, Kristian Kersting
Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications.
1 code implementation • NeurIPS 2023 • Marco Bellagente, Manuel Brack, Hannah Teufel, Felix Friedrich, Björn Deiseroth, Constantin Eichenberg, Andrew Dai, Robert Baldock, Souradeep Nanda, Koen Oostermeijer, Andres Felipe Cruz-Salinas, Patrick Schramowski, Kristian Kersting, Samuel Weinbach
The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users.
1 code implementation • 14 Apr 2023 • Felix Friedrich, David Steinmann, Kristian Kersting
Current machine learning models produce outstanding results in many areas but, at the same time, suffer from shortcut learning and spurious correlations.
1 code implementation • 16 Mar 2023 • Lukas Struppek, Dominik Hintersdorf, Felix Friedrich, Manuel Brack, Patrick Schramowski, Kristian Kersting
Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising concerns about their privacy.
1 code implementation • 7 Feb 2023 • Felix Friedrich, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Patrick Schramowski, Sasha Luccioni, Kristian Kersting
Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications.
1 code implementation • NeurIPS 2023 • Manuel Brack, Felix Friedrich, Dominik Hintersdorf, Lukas Struppek, Patrick Schramowski, Kristian Kersting
This leaves the user with little semantic control.
2 code implementations • 12 Dec 2022 • Manuel Brack, Patrick Schramowski, Felix Friedrich, Dominik Hintersdorf, Kristian Kersting
Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone.
1 code implementation • 19 Oct 2022 • Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting
In this work, we question the current common practice of storing all information in the model parameters and propose the Revision Transformer (RiT) to facilitate easy model updating.
2 code implementations • 19 Sep 2022 • Lukas Struppek, Dominik Hintersdorf, Felix Friedrich, Manuel Brack, Patrick Schramowski, Kristian Kersting
Models for text-to-image synthesis, such as DALL-E~2 and Stable Diffusion, have recently drawn a lot of interest from academia and the general public.
2 code implementations • 15 Sep 2022 • Dominik Hintersdorf, Lukas Struppek, Manuel Brack, Felix Friedrich, Patrick Schramowski, Kristian Kersting
Our large-scale experiments on CLIP demonstrate that individuals used for training can be identified with very high accuracy.
3 code implementations • 4 Mar 2022 • Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting
In addition, we discuss existing and introduce novel measures and benchmarks for evaluating the overall abilities of a XIL method.
1 code implementation • 2 Sep 2021 • Felix Friedrich, Patrick Schramowski, Christopher Tauchmann, Kristian Kersting
Transformer language models are state of the art in a multitude of NLP tasks.