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..
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model
Authors: Jean-Rémy Conti, Nathan Noiry, Stephan Clemencon, Vincent Despiegel, Stéphane Gentric
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive numerical experiments on a variety of datasets show that a careful selection significantly reduces gender bias. and 4. Numerical Experiments |
| Researcher Affiliation | Collaboration | 1LTCI, T el ecom Paris, Institut Polytechnique de Paris 2Idemia. |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | We plan to release the code used to conduct our experiments. |
| Open Datasets | Yes | It has been trained on the MS1M-Retina Face dataset (also called MS1MV3), introduced by (Deng et al., 2019b) in the ICCV 2019 Lightweight Face Recognition Challenge. and We choose IJB-C (Maze et al., 2018)... and All the models are evaluated on the LFW dataset (Huang et al., 2008). |
| Dataset Splits | No | The paper mentions training the Ethical Module on the training set used to train the pre-trained models and performing a grid-search for hyperparameter selection on IJB-C, but it does not provide explicit training, validation, or test dataset splits (percentages or counts) needed for reproduction. |
| Hardware Specification | Yes | Using one single GPU (NVIDIA RTX 3090), the computation of the embeddings takes 4 hours and each training takes 8 hours. |
| Software Dependencies | Yes | high precision using a Python library for arbitrary-precision floating-point arithmetic such as mpmath (Johansson et al., 2021; Kim, 2021). |
| Experiment Setup | Yes | The MLP within our Ethical Module has an input layer of 512 units... a shallow MLP of size (512, 1024, 512) with a Re LU activation after the first layer... For each experiment, we train the Ethical Module during 50 epochs with the Adam optimizer (Kingma & Ba, 2014). The batch size is set to 1024 and the learning rate to 0.01. |