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..
Semi-Supervised StyleGAN for Disentanglement Learning
Authors: Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit Patel, Animashree Anandkumar
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the impact of limited supervision and find that using only 0.25% 2.5% of labeled data is sufficient for good disentanglement on both synthetic and real datasets. We propose new metrics to quantify generator controllability, and observe there may exist a crucial trade-off between disentangled representation learning and controllable generation.3.1. Experimental Setup3.2. Key Results |
| Researcher Affiliation | Collaboration | 1Rice University ( Work done as a part of internship at Nvidia) 2Nvidia 3University of Toronto 4California Institute of Technology. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We consider two datasets to compare Info-Style GAN and state-of-the-art disentanglement models: d Sprites (Matthey et al., 2017) and our proposed Isaac3D (See details in Section 4.1). d Sprites is a commonly used dataset in disentanglement learning, with 737,280 images and each of resolution 64x64. |
| Dataset Splits | No | The paper does not explicitly provide training, validation, and test dataset splits with specific percentages or counts. It mentions varying the portion of labeled data (η) but not a standard train/validation/test split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions various models and hyperparameters used (e.g., β-VAE, Factor VAE, Info GAN-CR, Style GAN) and references their original papers, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions, CUDA, specific library versions). |
| Experiment Setup | Yes | For VAEs, we set β = 6 for β-VAE, γ = 30 for Factor VAE and β = 8 for β-TCVAE after a grid search over different hyperparameters. ... In experiments, we set ξ = 0.75 in Eq. (5) to be the same with (Berthelot et al., 2019). For the hyperparameters {γG, γE, β, α}, we find that setting γG = β = γ, γE = 0, α = 1 works well across different datasets, where we vary γ {1, 10}. |