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..
Generative Multi-Adversarial Networks
Authors: Ishan Durugkar, Ian Gemp, Sridhar Mahadevan
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Image generation tasks comparing the proposed framework to standard GANs demonstrate GMAN produces higher quality samples in a fraction of the iterations when measured by a pairwise GAM-type metric. |
| Researcher Affiliation | Academia | Ishan Durugkar , Ian Gemp , Sridhar Mahadevan College of Information and Computer Sciences University of Massachusetts, Amherst Amherst, MA 01060, USA EMAIL |
| Pseudocode | No | The paper describes the methods using text and mathematical equations, but it does not contain a dedicated 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code to reproduce experiments and plots is at https://github.com/iDurugkar/GMAN. |
| Open Datasets | Yes | We evaluate the aforementioned variations of GMAN on a variety of image generation tasks: MNIST (Le Cun et al. (1998)), CIFAR-10 (Krizhevsky (2009)) and Celeb A (Liu et al. (2015)). |
| Dataset Splits | No | The paper mentions training on MNIST, CIFAR-10, and Celeb A, but it does not explicitly provide details about the training, validation, and test dataset splits or how they were partitioned. |
| Hardware Specification | Yes | The code was written in Tensorflow (Abadi et al. (2016)) and run on Nvidia GTX 980 GPUs. |
| Software Dependencies | No | The paper mentions software like TensorFlow and Adam, but it does not provide specific version numbers for these software dependencies or any other libraries. |
| Experiment Setup | Yes | Specifics for the MNIST architecture and training are: Generator latent variables z U ( 1, 1)100 Generator convolution transpose layers: (4, 4, 128) , (8, 8, 64) , (16, 16, 32) , (32, 32, 1) Base Discriminator architecture: (32, 32, 1) , (16, 16, 32) , (8, 8, 64) , (4, 4, 128). Variants have either convolution 3 (4, 4, 128) removed or all the filter sizes are divided by 2 or 4. That is, (32, 32, 1) , (16, 16, 16) , (8, 8, 32) , (4, 4, 64) or (32, 32, 1) , (16, 16, 8) , (8, 8, 16) , (4, 4, 32). Re Lu activations for all the hidden units. Tanh activation at the output units of the generator. Sigmoid at the output of the Discriminator. Training was performed with Adam (Kingma & Ba (2014)) (lr = 2 10 4, β1 = 0.5). MNIST was trained for 20 epochs with a minibatch of size 100. Celeb A and CIFAR were trained over 24000 iterations with a minibatch of size 100. |