176 citations · 250 across the 54 of their papers we have counts for
13 papers · 1 filter
RL-FAT: Reinforcement Learning for Fair Adversarial Training
Tejaswini Medi, Levan Mikeladze, Margret Keuper
Deep neural networks remain highly vulnerable to adversarial perturbations, and adversarial training (AT) has become a widely used approach for improving robustness. However, impro…
A Granular Study of Safety Pretraining under Model Abliteration
Shashank Agnihotri, Jonas Jakubassa, Priyam Dey +4
Open-weight LLMs can be modified at inference time with simple activation edits, which raises a practical question for safety: do common safety interventions like refusal training…
TRIX- Trading Adversarial Fairness via Mixed Adversarial Training
Tejaswini Medi, Steffen Jung, Margret Keuper
Adversarial Training (AT) is a widely adopted defense against adversarial examples. However, existing approaches typically apply a uniform training objective across all classes, ov…
Towards Class-wise Robustness Analysis
Tejaswini Medi, Julia Grabinski, Margret Keuper
While being very successful in solving many downstream tasks, the application of deep neural networks is limited in real-life scenarios because of their susceptibility to domain sh…
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
Tejaswini Medi, Steffen Jung, Margret Keuper
Deep neural networks are susceptible to adversarial attacks and common corruptions, which undermine their robustness. In order to enhance model resilience against such challenges,…
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…
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…
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks
Steffen Jung, Margret Keuper
The minimum cost multicut problem is the NP-hard/APX-hard combinatorial optimization problem of partitioning a real-valued edge-weighted graph such as to minimize the total cost of…
Learning Where To Look -- Generative NAS is Surprisingly Efficient
Jovita Lukasik, Steffen Jung, Margret Keuper
The efficient, automated search for well-performing neural architectures (NAS) has drawn increasing attention in the recent past. Thereby, the predominant research objective is to…
Is Differentiable Architecture Search truly a One-Shot Method?
Jonas Geiping, Jovita Lukasik, Margret Keuper +1
Differentiable architecture search (DAS) is a widely researched tool for the discovery of novel architectures, due to its promising results for image classification. The main benef…
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
Jovita Lukasik, David Friede, Arber Zela +2
Neural architecture search (NAS) has recently been addressed from various directions, including discrete, sampling-based methods and efficient differentiable approaches. While the…
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks
Arber Zela, Julien Siems, Lucas Zimmer +3
The most significant barrier to the advancement of Neural Architecture Search (NAS) is its demand for large computational resources, which hinders scientifically sound empirical ev…
Unmasking DeepFakes with simple Features
Ricard Durall, Margret Keuper, Franz-Josef Pfreundt +1
Deep generative models have recently achieved impressive results for many real-world applications, successfully generating high-resolution and diverse samples from complex datasets…