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..
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Authors: Luisa M Zintgraf, Taco S Cohen, Tameem Adel, Max Welling
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the method in experiments on natural images (Image Net data), as well as medical images (MRI brain scans). |
| Researcher Affiliation | Academia | 1University of Amsterdam, 2Canadian Institute of Advanced Research, 3Vrije Universiteit Brussel EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Evaluating the prediction difference using conditional and multivariate sampling |
| Open Source Code | Yes | Our implementation is available at github.com/lmzintgraf/Deep Vis-Pred Diff. |
| Open Datasets | Yes | We use images from the ILSVRC challenge (Russakovsky et al., 2015) (a large dataset of natural images from 1000 categories) |
| Dataset Splits | Yes | to achieve an accuracy of 69.3% in a 10-fold cross-validation test. |
| Hardware Specification | No | No specific hardware details like exact GPU/CPU models or memory amounts used for computation were provided. The paper mentions 'using the GPU implementation of caffe' and 'on a CPU', and describes MRI scanner hardware: 'Subjects were scanned on two 3.0 Tesla scanner systems, 121 subjects on a Philips Intera system and 39 on a Philips Ingenia system.' |
| Software Dependencies | No | The paper mentions using 'caffe' and 'software developed in-house' for preprocessing, but does not specify version numbers for these or any other key libraries/solvers. |
| Experiment Setup | Yes | mini-batches with the standard settings of 10 samples and a window size of k = 10. |