How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
Authors: Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas Wallis, Wieland Brendel
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we test this hypothesis by asking humans to decide which of two square occlusions causes a larger change to a unit s activation. Both a large-scale crowdsourced experiment and measurements with experts show that on average the extremely activating feature visualizations by Olah et al. [40] indeed help humans on this task (68 4 % accuracy; baseline performance without any visualizations is 60 3 %). ... We run an extensive psychophysical experiment with more than 12, 000 trials distributed over 323 crowdsourced participants on Amazon Mechanical Turk (MTurk) and two experts |
| Researcher Affiliation | Academia | 1 Tübingen AI Center, University of Tübingen, Germany. 2 Institute of Psychology and Centre for Cognitive Science, Technical University of Darmstadt, Germany. |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented or labeled in the paper. |
| Open Source Code | Yes | Code and data are available at github.com/brendel-group/causal-understanding-via-visualizations. |
| Open Datasets | Yes | To generate stimuli, we follow Olah et al. [40] and use an Inception V1 network [53] trained on Image Net [12, 49]. ... Natural references: The reference images are the most strongly activating3 dataset samples from Image Net [12, 49]. |
| Dataset Splits | No | The paper mentions using ImageNet and a 'random subset of the training set (∼50%)' for activation computations, but does not specify the train/validation/test dataset splits used for training the InceptionV1 network or for partitioning the data in their human psychophysical experiment. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions using an 'Inception V1 network' and refers to other methods, but does not provide specific software dependencies with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | To generate stimuli, we follow Olah et al. [40] and use an Inception V1 network [53] trained on Image Net [12, 49]. ... We test units sampled from 9 layers and 2 Inception module branches (namely 3x3 and POOL). ... To generate query images, we place a square patch of 90 × 90 pixels of the average RGB color of the occluded pixels... We test the five different reference image types as separate experimental conditions. In each condition, we collect data from a total of 50 different MTurk participants... |