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..
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Authors: Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabas Poczos
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our evaluation on multiple benchmark datasets, including MNIST, CIFAR-10, Celeb A and LSUN, the performance of MMD GAN significantly outperforms GMMN, and is competitive with other representative GAN works. |
| Researcher Affiliation | Collaboration | Chun-Liang Li1, Wei-Cheng Chang1, Yu Cheng2 Yiming Yang1 Barnabás Póczos1 1 Carnegie Mellon University, 2AI Foundations, IBM Research EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: MMD GAN, our proposed algorithm. |
| Open Source Code | Yes | Our experiment code is available at https://github.com/October Chang/MMD-GAN. |
| Open Datasets | Yes | We train MMD GAN for image generation on the MNIST [32], CIFAR-10 [33], Celeb A [13], and LSUN bedrooms [12] datasets |
| Dataset Splits | Yes | We train MMD GAN for image generation on the MNIST [32], CIFAR-10 [33], Celeb A [13], and LSUN bedrooms [12] datasets. These are standard benchmark datasets which imply standard train/validation/test splits are used. |
| Hardware Specification | Yes | Figure 3 compares the computational time per generator iterations versus different B on Titan X. |
| Software Dependencies | No | The paper mentions using RMSProp [35] as an optimizer, but does not provide specific version numbers for any software libraries, frameworks, or programming languages used for implementation. |
| Experiment Setup | Yes | We use RMSProp [35] with learning rate of 0.00005 for a fair comparison with WGAN as suggested in its original paper [8]. We ensure the boundedness of model parameters of discriminator by clipping the weights point-wisely to the range [ 0.01, 0.01] as required by Assumption 2. The dimensionality h of the latent space is manually set according to the complexity of the dataset. We thus use h = 16 for MNIST, h = 64 for Celeb A, and h = 128 for CIFAR-10 and LSUN bedrooms. The batch size is set to be B = 64 for all datasets. |