Exemplary Natural Images Explain CNN Activations Better than
State-of-the-Art Feature Visualization

Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization

23 October 2020

Roland S. Zimmermann

Judith Schepers

Thomas S. A. Wallis

Matthias Bethge

Wieland Brendel

Papers citing "Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization"

8 / 8 papers shown

Title
iGAiVA: Integrated Generative AI and Visual Analytics in a Machine Learning Workflow for Text Classification Yuanzhe Jin Adrian Carrasco-Revilla Min Chen VLM 25 1 0 24 Sep 2024
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset Leon Sixt M. Schuessler Oana-Iuliana Popescu Philipp Weiß Tim Landgraf FAtt 24 14 0 25 Apr 2022
AI visualization in Nanoscale Microscopy A. Rajagopal V. Nirmala J. Andrew Karunya Institute of Technology 16 1 0 04 Jan 2022
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments M. Schuessler Philipp Weiß Leon Sixt 22 3 0 06 May 2021
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers Jacob Mitchell Springer Melanie Mitchell Garrett T. Kenyon AAML 16 13 0 09 Feb 2021
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 19 48 0 19 Oct 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,233 0 24 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017