Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
MGAN: Training Generative Adversarial Nets with Multiple Generators
Authors: Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung
ICLR 2018 | Venue PDF | LLM Run Details | Input Tokens: 23,558 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,579 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL 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. |