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..
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Authors: Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first explore the qualitative benefits of our method on image generation tasks: MNIST dataset (Le Cun et al., 1998) of hand-written digits and the Celeb A (Liu et al., 2015) dataset of human faces. Then for more quantitative evaluation we use the CIFAR-10 dataset (Krizhevsky & Hinton, 2009) and use the Inception Score introduced in (Salimans et al., 2016). |
| Researcher Affiliation | Academia | 1Princeton University, Princeton NJ 2Duke University, Durham NC. |
| Pseudocode | No | The paper describes the MIX+GAN protocol but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Related code is public online at https://github.com/ Princeton ML/MIX-plus-GANs.git |
| Open Datasets | Yes | MNIST dataset (Le Cun et al., 1998) of hand-written digits and the Celeb A (Liu et al., 2015) dataset of human faces. Then for more quantitative evaluation we use the CIFAR-10 dataset (Krizhevsky & Hinton, 2009) |
| Dataset Splits | No | The paper mentions training on datasets but does not explicitly provide training, validation, or test dataset splits (e.g., percentages or counts). |
| 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 software components and techniques (e.g., ADAM, DCGAN, WASSERSTEINGAN) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use exponentiated gradient (Kivinen & Warmuth, 1997): store the log-probabilities { ui, i 2 [T]}, and then obtain the weights by applying soft-max function on them: wui = e ui PT k=1 e uk , i 2 [T]. ... with learning rate lr = 0.0001. ... mixtures of 5 generators and 5 discriminators are used. |