85 citations · 182 across the 17 of their papers we have counts for
4 papers · 1 filter
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…
Don't trust your eyes: on the (un)reliability of feature visualizations
Robert Geirhos, Roland S. Zimmermann, Blair Bilodeau +2
How do neural networks extract patterns from pixels? Feature visualizations attempt to answer this important question by visualizing highly activating patterns through optimization…
Sensitivity of Slot-Based Object-Centric Models to their Number of Slots
Roland S. Zimmermann, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi +2
Self-supervised methods for learning object-centric representations have recently been applied successfully to various datasets. This progress is largely fueled by slot-based metho…
Provably Learning Object-Centric Representations
Jack Brady, Roland S. Zimmermann, Yash Sharma +3
Learning structured representations of the visual world in terms of objects promises to significantly improve the generalization abilities of current machine learning models. While…