Roland S. Zimmermann | DeepAI

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∙ 07/11/2023

Scale Alone Does not Improve Mechanistic Interpretability in Vision Models

In light of the recent widespread adoption of AI systems, understanding ...

0 Roland S. Zimmermann, et al. ∙

∙ 06/07/2023

Don't trust your eyes: on the (un)reliability of feature visualizations

How do neural networks extract patterns from pixels? Feature visualizati...

0 Robert Geirhos, et al. ∙

∙ 05/23/2023

Provably Learning Object-Centric Representations

Learning structured representations of the visual world in terms of obje...

0 Jack Brady, et al. ∙

∙ 06/28/2022

Increasing Confidence in Adversarial Robustness Evaluations

Hundreds of defenses have been proposed to make deep neural networks rob...

0 Roland S. Zimmermann, et al. ∙

∙ 06/23/2021

How Well do Feature Visualizations Support Causal Understanding of CNN Activations?

One widely used approach towards understanding the inner workings of dee...

26 Roland S. Zimmermann, et al. ∙

∙ 02/17/2021

Contrastive Learning Inverts the Data Generating Process

Contrastive learning has recently seen tremendous success in self-superv...

8 Roland S. Zimmermann, et al. ∙

∙ 10/23/2020

Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations

Feature visualizations such as synthetic maximally activating images are...

13 Judy Borowski, et al. ∙

∙ 07/01/2019

Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"

A recent paper by Liu et al. combines the topics of adversarial training...

0 Roland S. Zimmermann, et al. ∙

∙ 04/16/2019

Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks

We present Simion Zoo, a Reinforcement Learning (RL) workbench that prov...

0 Borja Fernandez-Gauna, et al. ∙

∙ 09/19/2018

Faster Training of Mask R-CNN by Focusing on Instance Boundaries

We present an auxiliary task to Mask R-CNN, an instance segmentation net...

6 Roland S. Zimmermann, et al. ∙

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