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..
Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning
Authors: Youngkyu Hong, Eunho Yang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our proposed methods significantly improve previous debiasing methods in various realistic datasets. and 4 Experiments We conduct experiments to evaluate how well our proposed method performs debiasing. |
| Researcher Affiliation | Collaboration | Youngkyu Hong Naver AI Lab EMAIL Eunho Yang KAIST, AITRICS EMAIL and This work was done as a student at KAIST. |
| Pseudocode | No | The paper describes mathematical formulations and processes but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code for our experiments is publicly available1. 1https://github.com/grayhong/bias-contrastive-learning |
| Open Datasets | Yes | For the case where the bias label is available, we evaluate the methods on Celeb A [31] and UTKFace [46], which have biases toward sensitive attributes such as gender or race. For the case where the bias label is unavailable, we use Image Net [36] and Image Net-A [23] to assess whether the bias of our model has been removed. and controlled experiment on Biased MNIST [3], where each digit is highly correlated with certain background color. We use Biased MNIST [3] dataset. As explained in Section 3.4, Biased MNIST is an MNIST [29] dataset |
| Dataset Splits | Yes | We explicitly construct a validation set and report the test unbiased accuracy at the epoch with the highest validation unbiased accuracy. and (from Appendix C.3) We split the original dataset into 80% train, 10% validation, and 10% test set by class-balanced sampling. We follow the same procedure for UTKFace dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions models like ResNet18 but does not provide specific version numbers for software dependencies or libraries used for the experiments. |
| Experiment Setup | Yes | We pre-train the bias-capturing model for 80 epochs. and full training of 120 epochs. and τ is a temperature hyperparameter. and α is a weight hyperparameter. and references to Appendix C.3 and C.5 for further details, which contain specific hyperparameters like learning rate, batch size, optimizer, and number of epochs. |