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..
Combating Representation Learning Disparity with Geometric Harmonization
Authors: Zhihan Zhou, Jiangchao Yao, Feng Hong, Ya Zhang, Bo Han, Yanfeng Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results on a range of benchmark datasets show the effectiveness of GH with high tolerance to the distribution skewness. Our code is available at https://github.com/Media Brain-SJTU/Geometric-Harmonization. |
| Researcher Affiliation | Collaboration | 1Cooperative Medianet Innovation Center, Shanghai Jiao Tong University 2Shanghai AI Laboratory 3Hong Kong Baptist University |
| Pseudocode | Yes | Finally, Eq. (3) can be analytically solved by Sinkhorn-Knopp algorithm [14] (refer to Appendix D for Algorithm 1). In Algorithm 2 of Appendix D, we give the complete implementation of our method. |
| Open Source Code | Yes | Our code is available at https://github.com/Media Brain-SJTU/Geometric-Harmonization. |
| Open Datasets | Yes | We use Res Net-18 [23] as the backbone for small-scale dataset (CIFAR-100-LT [5]) and Res Net-50 [23] for large-scale datasets (Image Net-LT [44], Places-LT [44]). |
| Dataset Splits | No | The paper states 'linear probing on a balanced dataset is used for evaluation' and references prior work, but does not explicitly provide specific training, validation, and test split percentages or sample counts for its experiments. |
| Hardware Specification | No | The paper describes the experimental setup, including datasets, baselines, and hyper-parameters, but does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using specific models like Res Net-18 and Res Net-50, and an SGD optimizer, but does not provide specific version numbers for any software dependencies (e.g., deep learning frameworks, libraries, or operating systems) used in the experiments. |
| Experiment Setup | Yes | For experiments on CIFAR-100-LT, we train model with the SGD optimizer, batch size 512, momentum 0.9 and weight decay factor 5 10 4 for 1000 epochs. For experiments on Image Net-LT and Places-LT, we only train for 500 epochs with the batch size 256 and weight decay factor 1 10 4. For learning rate schedule, we use the cosine annealing decay with the learning rate 0.5 1e 6 for all the baseline methods. As GH is combined with baselines, a proper warming-up of 500 epochs on CIFAR-100-LT and 400 epochs on Image Net-LT and Places-LT are applied. The cosine decay is set as 0.5 0.3, 0.3 1e 6 respectively. For hyper-parameters of GH, we provide a default setup across all the experiments: set the geometric dimension K as 100, w GH as 1 and the temperature γGH as 0.1. In the surrogate label allocation, we set the regularization coefficient λ as 20 and Sinkhorn iterations Es as 300. |