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..
Full-Gradient Representation for Neural Network Visualization
Authors: Suraj Srinivas, François Fleuret
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate the usefulness of Full Grad in explaining model behaviour with two quantitative tests: pixel perturbation and remove-and-retrain. |
| Researcher Affiliation | Academia | Suraj Srinivas Idiap Research Institute & EPFL EMAIL François Fleuret Idiap Research Institute & EPFL EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We use our pixel perturbation test to evaluate full-gradient saliency maps on the Imagenet 2012 validation dataset, using a VGG-16 model with batch normalization. We use ROAR to evaluate full-gradient saliency maps on the CIFAR100 dataset, using a 9-layer VGG model. |
| Dataset Splits | Yes | We use our pixel perturbation test to evaluate full-gradient saliency maps on the Imagenet 2012 validation dataset, using a VGG-16 model with batch normalization. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions models used (VGG-16, 9-layer VGG) and some post-processing parameters (e.g., bilinear Upsample, rescale, abs) for saliency map generation, but it does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs, optimizer settings) or system-level training settings for the neural networks. |