Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning
Authors: Youngkyu Hong, Eunho Yang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 youngkyu.hong@navercorp.com Eunho Yang KAIST, AITRICS eunhoy@kaist.ac.kr 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. |