Semi-Supervised StyleGAN for Disentanglement Learning
Authors: Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit Patel, Animashree Anandkumar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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}. |