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..
Diagnosing and Enhancing VAE Models
Authors: Bin Dai, David Wipf
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments from Sections 5 and 6 empirically corroborate motivational theory and reveal that the proposed two-stage procedure can generate high-quality samples... |
| Researcher Affiliation | Collaboration | Bin Dai Institute for Advanced Study Tsinghua University Beijing, China EMAIL David Wipf Microsoft Research Beijing, China EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found. The two-stage method is described in narrative text. |
| Open Source Code | Yes | The code for our model is available at https://github.com/daib13/Two Stage VAE. |
| Open Datasets | Yes | Testing is conducted across four significantly different datasets: MNIST (Le Cun et al., 1998), Fashion MNIST (Xiao et al., 2017), CIFAR-10 (Krizhevsky & Hinton, 2009) and Celeb A (Liu et al., 2015). |
| Dataset Splits | No | No explicit train/validation/test dataset splits (e.g., percentages or sample counts) are provided. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided. |
| Software Dependencies | No | All reported FID scores for VAE and GAN models were computed using Tensor Flow (https:// github.com/bioinf-jku/TTUR). No version number specified for TensorFlow. No other software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper states 'No effort was made to tune VAE training hyperparameters (e.g., learning rates, etc.); rather a single generic setting was ο¬rst agnostically selected and then applied to all VAE-like models', but does not provide specific values for these hyperparameters or detailed network architectures used in their experiments. |