Diffusion Visual Counterfactual Explanations
Authors: Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the quality of the DVCE. We compare DVCE to existing works in Sec. 4.1. In Sec. 4.2, we compare DVCEs for various state-of-the-art Image Net models and show how DVCEs can be used to interpret differences between classifiers. |
| Researcher Affiliation | Academia | Maximilian Augustin Valentyn Boreiko* Francesco Croce Matthias Hein University of Tübingen |
| Pseudocode | No | The paper references "Algorithm 1 of [14]" but does not contain its own pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available under https://github.com/valentyn1boreiko/DVCEs. |
| Open Datasets | Yes | Imange Net and Image Net21k are public datasets. |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset split percentages or sample counts. The checklist states "Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] We didn t train models." |
| Hardware Specification | No | The main paper text does not explicitly provide specific details about the hardware (e.g., GPU/CPU models) used for running the experiments. It refers to Appendix D for this information, which is not provided in the given text. |
| Software Dependencies | No | The paper mentions software such as "UNet", "StyleGAN2", "Swin-TF", "ConvNeXt", "EfficientNet", but it does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In all our experiments we use Cc = 0.1, and Cd = 0.15 unless we show ablations for one of the parameters. The angle for the cone projection is fixed to 30 . In our experiments, we set T = 200. |