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..
Using Stratified Sampling to Improve LIME Image Explanations
Authors: Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari, Damiano Verda
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the efficacy of the proposed approach. and provide empirical proofs of the advantage of using stratified sampling for LIME Image on a popular dataset. |
| Researcher Affiliation | Collaboration | 1University of Torino, Computer Science Department, C.so Svizzera 185, 10149 Torino, Italy 2Rulex Innovation Labs, Via Felice Romani 9, 16122 Genova, Italy |
| Pseudocode | Yes | Algorithm 1: Neighborhood sampling strategies |
| Open Source Code | Yes | The LIME Image with stratified sampling is available at: https://github.com/rashidrao-pk/lime stratified |
| Open Datasets | Yes | To better quantify the effect, we took the first 150 images of the Image Net Object Localization dataset (Addison Howard 2018). |
| Dataset Splits | No | The paper states it uses 'the first 150 images of the Image Net Object Localization dataset' but does not specify any train/validation/test splits for these images in the context of their experiments. |
| 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 | Yes | All code needed to replicate the experiments (including the requirements.txt with the library versions used) can be found at: https://github.com/rashidrao-pk/lime-stratified-examples |
| Experiment Setup | Yes | For each image we performed a dichotomic search on the max dist hyperparameter to find a configuration of quick shift that results in a number of superpixels k equal to 50, 100, 150 and 200. For each range, we run 10 times LIME Image with both the Monte Carlo and the stratified sampling using n=1000 samples....Using: kernel size = 4, max dist = 7, ratio = 0.2. and σ = 0.25 (by default) is the kernel width. |