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 [1].
Visualizing Deep Networks by Optimizing with Integrated Gradients
Authors: Zhongang Qi, Saeed Khorram, Li Fuxin11890-11898
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets show that the heatmaps produced by our approach are more correlated with the decision of the underlying deep network, in comparison with other state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | 1School of Electrical Engineering and Computer Science, Oregon State University 2Applied Research Center, PCG, Tencent EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: I-GOS |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the open-source code for the methodology described. |
| Open Datasets | Yes | We utilize the pretrained VGG19 (Simonyan and Zisserman 2015) and Resnet50 (He et al. 2016) networks from the Py Torch model zoo to test 5, 000 randomly selected images from the validation set of Image Net (Russakovsky et al. 2015). |
| Dataset Splits | Yes | We utilize the pretrained VGG19 (Simonyan and Zisserman 2015) and Resnet50 (He et al. 2016) networks from the Py Torch model zoo to test 5, 000 randomly selected images from the validation set of Image Net (Russakovsky et al. 2015). |
| Hardware Specification | Yes | For each approach, we only use one Nvidia 1080Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch model zoo' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In Eq. (8), β = 0.0001. λ1 and λ2 in Eq. (9) were fixed across all experiments under the same heatmap resolution. We downloaded and ran the code for most baselines, except for (Sundararajan, Taly, and Yan 2017) which we implemented. All baselines were tuned to best performances. For RISE, we followed (Petsiuk, Das, and Saenko 2018) to generate 4, 000 7 7 random samples for VGG, and 8, 000 7 7 random samples for Res Net. For IGOS, the maximal iteration is 15; for Mask, the maximal iteration is 500. |