Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Authors: Aditya Grover, Manik Dhar, Stefano Ermon
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 fidelity in the generated samples. |
| Researcher Affiliation | Academia | Aditya Grover, Manik Dhar, Stefano Ermon Department of Computer Science Stanford University {adityag, dmanik, ermon}@cs.stanford.edu |
| 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. |