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..
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Authors: Lars Mescheder, Sebastian Nowozin, Andreas Geiger
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that our model is able to learn rich posterior distributions and show that the model is able to generate compelling samples for complex data sets. |
| Researcher Affiliation | Collaboration | 1Autonomous Vision Group, MPI T ubingen 2Microsoft Research Cambridge 3Computer Vision and Geometry Group, ETH Z urich. Correspondence to: Lars Mescheder <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Adversarial Variational Bayes (AVB) |
| Open Source Code | No | The paper does not provide any links to source code or state that code is made available. |
| Open Datasets | Yes | We applied this to the Eight School example from Gelman et al. (2014). [...] In addition, we trained deep convolutional networks based on the DC-GAN-architecture (Radford et al., 2015) on the binarized MNIST-dataset (Le Cun et al., 1998). An additional experiment on the celeb A dataset (Liu et al., 2015) can be found in the Supplementary Material. |
| Dataset Splits | No | The paper mentions a test set size for MNIST but does not provide complete training/validation/test splits, percentages, or refer to a standard split that includes all three for any dataset. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions tools like STAN and ITE-package, but does not provide specific version numbers for these or other general software dependencies (e.g., Python, deep learning frameworks) used in their experimental setup. |
| Experiment Setup | Yes | Both the encoder and decoder are parameterized by 2-layer fully connected neural networks with 512 hidden units each. [...] The adversary is parameterized by two neural networks with two 512-dimensional hidden layers each [...]. [...] For the decoder network, we use a 5-layer deep convolutional neural network. [...] For the adversary, we replace the fully connected neural network acting on z and x with a fully connected 4-layer neural networks with 1024 units in each hidden layer. [...] For every posterior update step we performed two steps for the adversary. |