Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Diffusion Visual Counterfactual Explanations
Authors: Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein
NeurIPS 2022 | Venue PDF | 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. |