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..
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
Authors: Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a range of experiments on several datasets and DGNs, e.g., for the state-of-the-art Style GAN2 trained on the FFHQ dataset, uniform sampling via Ma GNET increases distribution precision by 4.1% and recall by 3.0% and decreases gender bias by 41.2%, without requiring labels or retraining. |
| Researcher Affiliation | Academia | Ahmed Imtiaz Humayun Rice University EMAIL Randall Balestriero Rice University EMAIL Richard Baraniuk Rice University EMAIL |
| Pseudocode | Yes | Algorithm 1: Ma GNET Sampling as described in Sec. 3.2 |
| Open Source Code | Yes | Colab and codes at bit.ly/magnet-sampling |
| Open Datasets | Yes | For example, the Celeb A dataset contains a large fraction of smiling faces. |
| Dataset Splits | No | The paper refers to using existing datasets for training and evaluation but does not explicitly provide details about the specific training, validation, and test dataset splits used for reproducibility. |
| Hardware Specification | Yes | All the experiments were run on a Quadro RTX 8000 GPU, which has 48 GB of high-speed GDDR6 memory and 576 Tensor cores. |
| Software Dependencies | Yes | In short, we employed TF2 (2.4 at the time of writing), all the usual Python scientific libraries such as Num Py and Py Torch. |
| Experiment Setup | Yes | For Style GAN2, we use the official config-e provided in the Git Hub Style GAN2 repo1, unless specified. We use the recommended default of ψ = 0.5 as the interpolating stylespace truncation, to ensure generation quality of faces for the qualitative experiments. |