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..
Perceptual Generative Autoencoders
Authors: Zijun Zhang, Ruixiang Zhang, Zongpeng Li, Yoshua Bengio, Liam Paull
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of LPGA and VPGA on three image datasets, MNIST (Le Cun et al., 1998), CIFAR-10 (Krizhevsky & Hinton, 2009), and Celeb A (Liu et al., 2015). For each model and each dataset, we take 5,000 generated samples to compute the FID score. The results (with standard errors of 3 or more runs) are summarized in Table. 1. |
| Researcher Affiliation | Academia | 1University of Calgary, Canada 2MILA, Universit e de Montr eal, Canada 3Wuhan University, China. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/zj10/PGA. |
| Open Datasets | Yes | In this section, we evaluate the performance of LPGA and VPGA on three image datasets, MNIST (Le Cun et al., 1998), CIFAR-10 (Krizhevsky & Hinton, 2009), and Celeb A (Liu et al., 2015). |
| Dataset Splits | No | The paper mentions tuning hyperparameters heuristically but does not provide specific train/validation/test dataset split percentages, counts, or explicit references to standard splits used for validation. |
| Hardware Specification | No | All experiments are performed on a single GPU. This statement is too general and does not provide specific model numbers or types of GPU, CPU, or other hardware components. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | SGD with a momentum of 0.9 is used to train all models. For LPGA, γ (Eq. (9)) tends to vary in a small range for different datasets (e.g., 1.5e 2 for MNIST and CIFAR-10, and 1e 2 for Celeb A). For VPGA, η (Eq. (13)) can vary widely (e.g., 2e 2 for MNIST, 3e 2 for CIFAR-10, and 2e 3 for Celeb A), and thus is slightly more difficult to tune. |