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..
Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes
Authors: Fengda Zhang, Kun Kuang, Long Chen, Yuxuan Liu, Chao Wu, Jun Xiao
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results illustrate the effectiveness of our method in terms of fairness and utility, even with very limited sensitive attributes and serious data bias. |
| Researcher Affiliation | Collaboration | Fengda Zhang1, Kun Kuang1,2 , Long Chen3, Yuxuan Liu1, Chao Wu1, Jun Xiao1 1Zhejiang University, 2Key Laboratory for Corneal Diseases Research of Zhejiang Province 3The Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 Semi-supervised Algorithm for Learning Classifier and Generator |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We validate our method on the following datasets: 1) Celeb A (Liu et al., 2018) is a dataset with over 200k facial images... 2) UTK-Face (Zhang et al., 2017) contains over 20k facial images... Dogs and Cats (dog, 2013) |
| Dataset Splits | No | The paper discusses training and testing sets, but does not explicitly describe a separate validation set split (e.g., percentages or counts for validation data). |
| Hardware Specification | No | The paper mentions running experiments but does not provide specific details on hardware components such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions using specific models and architectures like '5-layer CNN' and 'Res Net-18', but does not specify version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | We resize the images of Celeb A and UTK-Face to 128 128, and use a 5-layer CNN (Krizhevsky et al., 2017) as the encoder of generative model. Besides, the decoder also has 5 layers... We use the Res Net-18 (He et al., 2016) as encoder model and a MLP as projection head, and train them via weighted fairness-aware contrastive loss for 100 epochs. Afterwards, we train a linear classifier on top of the frozen representation given by encoder F( ) for 10 epochs on the training dataset. |