MGAN: Training Generative Adversarial Nets with Multiple Generators
Authors: Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and Image Net) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by the generators. |
| Researcher Affiliation | Academia | Quan Hoang University of Massachusetts-Amherst Amherst, MA, USA qhoang@umass.edu Tu Dinh Nguyen, Trung Le, Dinh Phung PRa DA Centre, Deakin University Geelong, Australia {tu.nguyen,trung.l,dinh.phung} @deakin.edu.au |
| Pseudocode | Yes | We refer to Appendix A for the pseudo-code and algorithms for parameter learning for our proposed MGAN. |
| Open Source Code | Yes | We use Tensor Flow (Abadi et al., 2016) to implement our model, and the source code is available at: https://github.com/qhoangdl/MGAN. |
| Open Datasets | Yes | We use 3 widely-adopted datasets: CIFAR-10 (Krizhevsky & Hinton, 2009), STL-10 (Coates et al., 2011) and Image Net (Russakovsky et al., 2015). |
| Dataset Splits | Yes | We use 3 widely-adopted datasets: CIFAR-10 (Krizhevsky & Hinton, 2009), STL-10 (Coates et al., 2011) and Image Net (Russakovsky et al., 2015). CIFAR-10 contains 50,000 32 32 training images of 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments with specific details such as GPU models, CPU models, or memory. |
| Software Dependencies | No | The paper mentions using 'Tensor Flow (Abadi et al., 2016)' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For all experiments, we use: (i) shared parameters among generators in all layers except for the input layer; (ii) shared parameters between discriminator and classifier in all layers except for the output layer; (iii) Adam optimizer (Kingma & Ba, 2014) with learning rate of 0.0002 and the first-order momentum of 0.5; (iv) minibatch size of 64 samples for training discriminators; (v) Re LU activations (Nair & Hinton, 2010) for generators; (vi) Leaky Re LU (Maas et al., 2013) with slope of 0.2 for discriminator and classifier; and (vii) weights randomly initialized from Gaussian distribution N(0, 0.02I) and zero biases. |