Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Authors: Luisa M Zintgraf, Taco S Cohen, Tameem Adel, Max Welling
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | 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 {lmzintgraf,tameem.hesham}@gmail.com, {t.s.cohen, m.welling}@uva.nl |
| 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. |