Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition
Authors: Sangyu Han, Yearim Kim, Nojun Kwak
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 {acoexist96,yerim1656,nojunk}@snu.ac.kr |
| 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. |