Generative Multi-Adversarial Networks
Authors: Ishan Durugkar, Ian Gemp, Sridhar Mahadevan
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | 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 {idurugkar,imgemp,mahadeva}@cs.umass.edu |
| 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. |