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..
ChatGPT-Powered Hierarchical Comparisons for Image Classification
Authors: Zhiyuan Ren, Yiyang Su, Xiaoming Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments and analyses, we demonstrate that our proposed approach is intuitive, effective, and explainable. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, Michigan State University East Lansing, MI 48824 EMAIL |
| Pseudocode | Yes | Figure 3: Psuedo-code for building the knowledge trees. |
| Open Source Code | Yes | Code is available here. |
| Open Datasets | Yes | We conduct experiments on six different image classification benchmarks, i.e. Image Net [10], CUB [43], Food101 [2], Place365 [26], Oxford Pets [50], and Describable Textures [7] |
| Dataset Splits | Yes | In line with the methodology employed by [29], we expand the Image Net validation by introducing two new categories, each containing five additional images. |
| Hardware Specification | Yes | On a single Nvidia RTX A6000 GPU, it is feasible to replicate all the results of our paper within approximately two hours. |
| Software Dependencies | No | The paper mentions using 'CLIP Vi T-L/14' and 'Chat GPT' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | There are four hyperparameters in our method, including the number of groups N in the k-means algorithm [27], the threshold l for leaf nodes, the weight λ assigned to score offset, and the tolerance τ for score reduction. ... We generally set l to 2 or 3 and τ to 0. |