ChatGPT-Powered Hierarchical Comparisons for Image Classification
Authors: Zhiyuan Ren, Yiyang Su, Xiaoming Liu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 {renzhiy1, suyiyan1, liuxm}@msu.edu |
| 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. |