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..
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Authors: Aditya Grover, Manik Dhar, Stefano Ermon
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on MNIST and CIFAR-10 demonstrate that hybrid training can attain high held-out likelihoods while retaining visual ๏ฌdelity in the generated samples. |
| Researcher Affiliation | Academia | Aditya Grover, Manik Dhar, Stefano Ermon Department of Computer Science Stanford University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for reproducing the results is available at https://github.com/ermongroup/flow-gan. |
| Open Datasets | Yes | We compare learning of Flow-GANs using MLE and adversarial learning (ADV) for the MNIST dataset of handwritten digits (Le Cun, Cortes, and Burges 2010) and the CIFAR-10 dataset of natural images (Krizhevsky and Hinton 2009). |
| Dataset Splits | No | The paper mentions 'validation NLLs' and 'train NLLs' in Section 3.3 and refers to MNIST and CIFAR-10 datasets, but it does not explicitly state the specific training/validation/test split percentages or sample counts in the provided text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions various models and architectures (e.g., DCGAN, NICE, Real-NVP) but does not provide specific software dependencies with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | No | The paper mentions chosen architectures (NICE, Real-NVP) and divergences (Wasserstein distance) and states that 'Further experimental details are provided in a companion technical report', indicating that specific hyperparameters or training configurations are not in the main text. |