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..
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Authors: Jiawei Ren, Cunjun Yu, shunan sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, hongsheng Li
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks. |
| Researcher Affiliation | Collaboration | Jiawei Ren1, Cunjun Yu1, Shunan Sheng1,2, Xiao Ma1,3, Haiyu Zhao1*, Shuai Yi1, Hongsheng Li4 1 Sense Time Research 2 Nanyang Technological University 3 National University of Singapore 4 Multimedia Laboratory, The Chinese University of Hong Kong EMAIL EMAIL EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks that are clearly labeled or formatted like code. |
| Open Source Code | Yes | Corresponding author Code available at https://github.com/jiawei-ren/Balanced Meta Softmax |
| Open Datasets | Yes | We perform experiments on long-tailed image classification datasets, including CIFAR-10-LT [18], CIFAR-100-LT [18], Image Net-LT [23] and Places-LT [34] and one long-tailed instance segmentation dataset, LVIS [7]. |
| Dataset Splits | Yes | For classification tasks, after training on the long-tailed dataset, we evaluate the models on the corresponding balanced test/validation dataset and report top-1 accuracy. We create the meta set by class-balanced sampling from the training set Dtrain. For LVIS, we use official training and testing splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | Our implementation details can be found in the supplementary materials. |