6 citations · 6 across the 5 of their papers we have counts for
6 papers
Low-Pass Filtering Improves Behavioral Alignment of Vision Models
Max Wolff, Thomas Klein, Evgenia Rusak +2
Despite their impressive performance on computer vision benchmarks, Deep Neural Networks (DNNs) still fall short of adequately modeling human visual behavior, as measured by error…
MentisOculi: Revealing the Limits of Reasoning with Mental Imagery
Jana Zeller, Thaddäus Wiedemer, Fanfei Li +6
Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interlea…
Quantifying Uncertainty in Error Consistency: Towards Reliable Behavioral Comparison of Classifiers
Thomas Klein, Sascha Meyen, Wieland Brendel +2
Benchmarking models is a key factor for the rapid progress in machine learning (ML) research. Thus, further progress depends on improving benchmarking metrics. A standard metric to…
LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models
Fanfei Li, Thomas Klein, Wieland Brendel +2
Out-of-distribution (OOD) robustness is a desired property of computer vision models. Improving model robustness requires high-quality signals from robustness benchmarks to quantif…
How Aligned are Different Alignment Metrics?
Jannis Ahlert, Thomas Klein, Felix Wichmann +1
In recent years, various methods and benchmarks have been proposed to empirically evaluate the alignment of artificial neural networks to human neural and behavioral data. But how…
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
Roland S. Zimmermann, Thomas Klein, Wieland Brendel
In light of the recent widespread adoption of AI systems, understanding the internal information processing of neural networks has become increasingly critical. Most recently, mach…