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..
Fair Generative Modeling via Weak Supervision
Authors: Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate the efficacy of our approach which reduces bias w.r.t. latent factors by an average of up to 34.6% over baselines for comparable image generation using generative adversarial networks. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University 2Department of Statistics, Stanford University. |
| Pseudocode | Yes | Algorithm 1 Learning Fair Generative Models |
| Open Source Code | Yes | We provide reference implementations in Py Torch (Paszke et al., 2017), and the codebase for this work is open-sourced at https://github.com/ermongroup/fairgen. |
| Open Datasets | Yes | We consider the Celeb A (Ziwei Liu & Tang, 2015) dataset, which is commonly used for benchmarking deep generative models and comprises of images of faces with 40 labeled binary attributes. |
| Dataset Splits | Yes | For both models, we use a variant of Res Net18 (He et al., 2016) on the standard train and validation splits of Celeb A. |
| Hardware Specification | No | For both models, we use a variant of Res Net18 (He et al., 2016) on the standard train and validation splits of Celeb A. For the generative model, we used a Big GAN (Brock et al., 2018) trained to minimize the hinge loss (Lim & Ye, 2017; Tran et al., 2017) objective. |
| Software Dependencies | Yes | We provide reference implementations in Py Torch (Paszke et al., 2017) |
| Experiment Setup | Yes | Additional details regarding the architectural design and hyperparameters in Supplement C. |