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..
Improving Generative Moment Matching Networks with Distribution Partition
Authors: Yong Ren, Yucen Luo, Jun Zhu9403-9410
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that GMMN-DP can generate complex samples on datasets such as Celeb A and CIFAR-10, where the vanilla GMMN fails. We present experimental results of the generative task on the commonly used datasets MNIST (Le Cun et al. 1998), Celeb A (Liu et al. 2015) and CIFAR-10. |
| Researcher Affiliation | Academia | Yong Ren,1 Yucen Luo, 1 Jun Zhu 1 1 Department of Computer Science and Technology, Institute for AI, BNRist Center, Tsinghua University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The algorithm is summarized in Alg. 1. Algorithm 1: Stochastic training for GMMN-DP |
| Open Source Code | Yes | The code can be found HERE2. 2https://github.com/McGrady00H/Improving-MMD-with-Distribution-Partition |
| Open Datasets | Yes | We present experimental results of the generative task on the commonly used datasets MNIST (Le Cun et al. 1998), Celeb A (Liu et al. 2015) and CIFAR-10. |
| Dataset Splits | No | The paper describes mini-batch training but does not specify explicit training, validation, and test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | Yes | In our experimental settings with a single RTX 2080ti GPU, the average time per iteration on CIFAR10 with B = 64 and model size 4, 300MB is 0.25s. |
| Software Dependencies | No | The paper mentions software components but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | Hyper-parameters: We use a mixture of 7 RBF kernels K(x, x ) = P7 i=1 KĻi(x, x ) with Ļi to be {1, 4, 8, 16, 24, 32, 64} for the sample space and K(y, y ) to be RBF kernel with Ļ = 1. The model is optimized using Adam with learning rate 0.001 and Ī² = (0.9, 0.999). The batch size B is set to be 64 for all the datasets. The regularization parameter Ī» for b CX|Y is set to be 0.01. The dimension for the additional randomness z is set to be 2. |