34 citations · 212 across the 70 of their papers we have counts for
21 papers · 1 filter
LEDITS++: Limitless Image Editing using Text-to-Image Models
Manuel Brack, Felix Friedrich, Katharina Kornmeier +4
Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research…
Divergent Token Metrics: Measuring degradation to prune away LLM components -- and optimize quantization
Björn Deiseroth, Max Meuer, Nikolas Gritsch +4
Large Language Models (LLMs) have reshaped natural language processing with their impressive capabilities. However, their ever-increasing size has raised concerns about their effec…
Defending Our Privacy With Backdoors
Dominik Hintersdorf, Lukas Struppek, Daniel Neider +1
The proliferation of large AI models trained on uncurated, often sensitive web-scraped data has raised significant privacy concerns. One of the concerns is that adversaries can ext…
Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
Lukas Struppek, Dominik Hintersdorf, Kristian Kersting
Label smoothing -- using softened labels instead of hard ones -- is a widely adopted regularization method for deep learning, showing diverse benefits such as enhanced generalizati…
Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data
Lukas Struppek, Martin B. Hentschel, Clifton Poth +2
Backdoor attacks pose a serious security threat for training neural networks as they surreptitiously introduce hidden functionalities into a model. Such backdoors remain silent dur…
Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge
Manuel Brack, Patrick Schramowski, Kristian Kersting
Text-conditioned image generation models have recently achieved astonishing image quality and alignment results. Consequently, they are employed in a fast-growing number of applica…
Learning by Self-Explaining
Wolfgang Stammer, Felix Friedrich, David Steinmann +3
Much of explainable AI research treats explanations as a means for model inspection. Yet, this neglects findings from human psychology that describe the benefit of self-explanation…
Balancing Transparency and Risk: The Security and Privacy Risks of Open-Source Machine Learning Models
Dominik Hintersdorf, Lukas Struppek, Kristian Kersting
The field of artificial intelligence (AI) has experienced remarkable progress in recent years, driven by the widespread adoption of open-source machine learning models in both rese…
Learning to Intervene on Concept Bottlenecks
David Steinmann, Wolfgang Stammer, Felix Friedrich +1
While deep learning models often lack interpretability, concept bottleneck models (CBMs) provide inherent explanations via their concept representations. Moreover, they allow users…
Self-Expanding Neural Networks
Rupert Mitchell, Robin Menzenbach, Kristian Kersting +1
The results of training a neural network are heavily dependent on the architecture chosen; and even a modification of only its size, however small, typically involves restarting th…
ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models
Sophie Jentzsch, Kristian Kersting
Humor is a central aspect of human communication that has not been solved for artificial agents so far. Large language models (LLMs) are increasingly able to capture implicit and c…
Masked Autoencoders are Efficient Continual Federated Learners
Subarnaduti Paul, Lars-Joel Frey, Roshni Kamath +2
Machine learning is typically framed from a perspective of i.i.d., and more importantly, isolated data. In parts, federated learning lifts this assumption, as it sets out to solve…
Deep Classifier Mimicry without Data Access
Steven Braun, Martin Mundt, Kristian Kersting
Access to pre-trained models has recently emerged as a standard across numerous machine learning domains. Unfortunately, access to the original data the models were trained on may…
Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World's Ugliness?
Manuel Brack, Felix Friedrich, Patrick Schramowski +1
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 ap…
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation
Marco Bellagente, Manuel Brack, Hannah Teufel +11
The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users. The intended generation can be expressed in…
One Explanation Does Not Fit XIL
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. To address such flaws, the…
Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations
Lukas Struppek, Dominik Hintersdorf, Felix Friedrich +3
Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising conc…
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
Felix Friedrich, Manuel Brack, Lukas Struppek +4
Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications. However, since they are highly da…
Probabilistic Circuits That Know What They Don't Know
Fabrizio Ventola, Steven Braun, Zhongjie Yu +2
Probabilistic circuits (PCs) are models that allow exact and tractable probabilistic inference. In contrast to neural networks, they are often assumed to be well-calibrated and rob…
SEGA: Instructing Text-to-Image Models using Semantic Guidance
Manuel Brack, Felix Friedrich, Dominik Hintersdorf +3
Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot ge…
AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation
Björn Deiseroth, Mayukh Deb, Samuel Weinbach +3
Generative transformer models have become increasingly complex, with large numbers of parameters and the ability to process multiple input modalities. Current methods for explainin…