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..
Sobolev GAN
Authors: Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically study Sobolev GAN in character level text generation (Section 6.1). We finally show in Section 6.2 that a variant of Sobolev GAN achieves competitive semisupervised learning results on CIFAR-10, thanks to the smoothness enforced on the critic by Sobolev GAN which relates to Laplacian regularization. |
| Researcher Affiliation | Collaboration | Youssef Mroueh , Chun-Liang Li , , Tom Sercu , , Anant Raj , & Yu Cheng IBM Research AI Carnegie Mellon University Max Planck Institute for Intelligent Systems denotes Equal Contribution EMAIL, EMAIL, EMAIL,EMAIL |
| Pseudocode | Yes | Algorithm 1 Sobolev GAN |
| Open Source Code | Yes | Code for semi-supervised learning experiments is available on https://github.com/tomsercu/SobolevGAN-SSL |
| Open Datasets | Yes | semi-supervised learning on CIFAR-10 |
| Dataset Splits | No | We do hyperparameter and model selection on the validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with explicit version tags) were mentioned. |
| Experiment Setup | Yes | We use Adam with learning rate η = 2e 4, β1 = 0.5 and β2 = 0.999, both for critic f (without BN) and Generator (with BN). We train all models for 350 epochs. We used some L2 weight decay: 1e 6 on ω, S (i.e. all layers except last) and 1e 3 weight decay on the last layer v. For formulation 1 (Fisher only) we have ρF = 1e 7, modified critic learning rate ηD = 1e 4, critic iters nc = 2. For formulation 2 (Sobolev + Fisher) we have ρF = 5e 8, ρS = 2e 8, critic iters nc = 1. For the WGAN-GP (Gulrajani et al., 2017) baseline SSL experiment we followed the original paper with critic iters nc = 5, ηG = ηD = 1e 4, Adam β2=0.9 and GP weight λGP = 10.0. The noise level σ was annealed following a linear schedule starting from an initial noise level σ0 (at iteration i, σi = σ0(1 i Maxiter), Maxiter=30K). |