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..
Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition
Authors: Sangyu Han, Yearim Kim, Nojun Kwak
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conducted a comprehensive comparative analysis involving our proposed method, SRD, and several state-of-the-art methods: Saliency (Simonyan et al., 2014), Guided Backprop (Springenberg et al., 2015), Grad Input (Ancona et al., 2018), Inte Grad (Sundararajan et al., 2017), LRPz+ (Montavon et al., 2017), Smoothgrad (Smilkov et al., 2017), Fullgrad (Srinivas & Fleuret, 2019), Grad CAM (Selvaraju et al., 2017), Grad CAM++ (Chattopadhay et al., 2018), Score CAM (Wang et al., 2020), Ablation CAM (Ramaswamy et al., 2020), XGrad CAM (Fu et al., 2020), and Layer CAM (Jiang et al., 2021). |
| Researcher Affiliation | Academia | Sangyu Han , Yearim Kim , Nojun Kwak Seoul National University Seoul, 08826, Korea EMAIL |
| Pseudocode | Yes | Algorithm 1 APOP process in Py Torch pseudocode |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | All evaluations were carried out on the Image Net-S50 dataset (Gao et al., 2022), which contains 752 samples along with object segmentation masks. |
| Dataset Splits | Yes | All of our APOP experiments were conducted on the Image Net validation dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | The dimensions of the resulting saliency maps were as follows: (7, 7) for low-resolution, (28, 28) for high-resolution, and (224, 224) for input-scale. |