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..
Autoencoding beyond pixels using a learned similarity metric
Authors: Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we investigate the performance of different generative models: Plain VAE with an element-wise Gaussian observation model. VAE with a learned distance (VAEDisl). The combined VAE/GAN model. A GAN. We apply our methods to face images from the Celeb A dataset2 (Liu et al., 2015). |
| Researcher Affiliation | Collaboration | Anders Boesen Lindbo Larsen1 EMAIL Søren Kaae Sønderby2 EMAIL Hugo Larochelle3 EMAIL Ole Winther1,2 EMAIL 1 Department for Applied Mathematics and Computer Science, Technical University of Denmark 2 Bioinformatics Centre, Department of Biology, University of Copenhagen, Denmark 3 Twitter, Cambridge, MA, USA |
| Pseudocode | Yes | Algorithm 1 Training the VAE/GAN model |
| Open Source Code | Yes | We refer to our implementation available online1. 1http://github.com/andersbll/ autoencoding_beyond_pixels |
| Open Datasets | Yes | We apply our methods to face images from the Celeb A dataset2 (Liu et al., 2015). |
| Dataset Splits | No | The paper mentions a 'training set' and 'test set' for the LFW dataset, but does not explicitly provide details about a 'validation' split or specify percentages/counts for any data splits. |
| Hardware Specification | No | The paper mentions 'Nvidia for donating GPUs used in experiments' but does not specify the exact GPU model or any other hardware components like CPU, memory, or specific machine configurations. |
| Software Dependencies | No | The paper mentions using 'Deep Py3 and CUDArray' as software frameworks and cites a technical report for CUDArray, but it does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Our models are trained with RMSProp using a learning rate of 0.0003 and a batch size of 64. In table 1 we list the network architectures. |