Search Results for author: Felix Friedrich

Found 16 papers, 12 papers with code

Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You

1 code implementation29 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.

Multilingual Text-to-Image Generation Prompt Engineering +1

Learning by Self-Explaining

no code implementations15 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.

Image Classification

Learning to Intervene on Concept Bottlenecks

no code implementations25 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.

Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World's Ugliness?

no code implementations28 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.

Image Generation

One Explanation Does Not Fit XIL

1 code implementation14 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.

Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations

1 code implementation16 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.

Attribute Face Recognition +2

Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness

1 code implementation7 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.

Fairness Text-to-Image Generation

The Stable Artist: Steering Semantics in Diffusion Latent Space

2 code implementations12 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.

Image Generation

Revision Transformers: Instructing Language Models to Change their Values

1 code implementation19 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.

Information Retrieval Retrieval +1

Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis

2 code implementations19 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.

Image Generation

Does CLIP Know My Face?

2 code implementations15 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.

Inference Attack

A Typology for Exploring the Mitigation of Shortcut Behavior

3 code implementations4 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.

BIG-bench Machine Learning

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