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..
Explain Any Concept: Segment Anything Meets Concept-Based Explanation
Authors: Ao Sun, Pingchuan Ma, Yuanyuan Yuan, Shuai Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation over two popular datasets (Image Net and COCO) illustrate the highly encouraging performance of EAC over commonly-used XAI methods. |
| Researcher Affiliation | Academia | Ao Sun, Pingchuan Ma, Yuanyuan Yuan, and Shuai Wang The Hong Kong University of Science and Technology EMAIL |
| Pseudocode | No | The paper includes a technical pipeline diagram (Figure 1) but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Open Source. We publicly release and maintain EAC under the following github page: https://github.com/Jerry00917/samshap. |
| Open Datasets | Yes | We evaluate EAC on two popular datasets, Image Net [38] and COCO [39] |
| Dataset Splits | Yes | We use the standard training/validation split for both datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper does not specify version numbers for key software components or libraries used in the experiments. |
| Experiment Setup | Yes | The only hyper-parameter considered in EAC is when fitting the PIE Scheme, i.e. a simple linear neural network learning scheme and the Monte Carlo (MC) sampling: we set lr = 0.008 and the number of MC sampling as 50000 throughout all experiments. |