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 GAN with Neighbors Embedding and Gradient Matching
Authors: Ngoc-Trung Tran, Tuan-Anh Bui, Ngai-Man Cheung5191-5198
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of 30.80 for the STL-10 dataset. |
| Researcher Affiliation | Collaboration | Ngoc-Trung Tran, Tuan-Anh Bui, Ngai-Man Cheung ST Electronics SUTD Cyber Security Laboratory Singapore University of Technology and Design |
| Pseudocode | Yes | Algorithm 1 Our GN-GAN model |
| Open Source Code | Yes | Our code is available at: https://github.com/tntrung/gan |
| Open Datasets | Yes | When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets |
| Dataset Splits | No | The paper discusses training data and generated samples for evaluation (e.g., '10K real samples and 5K generated samples' for FID), but does not explicitly provide percentages or counts for a distinct validation dataset split from the original data that would be used during training for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | To examine the computational time of gradient matching of our proposed generator objective, we measure its training time for one mini-batch (size 64) with/without GM (Computer: Intel Xeon Octa-core CPU E5-1260 3.7GHz, 64GB RAM, GPU Nvidia 1080Ti) with CNN for CIFAR-10. |
| Software Dependencies | No | Tensorflow is mentioned as the framework for computation ('can be computed easily in Tensorflow'), but no specific version number for Tensorflow or any other software dependencies is provided. |
| Experiment Setup | Yes | The number of neurons for each hidden layer is 4, the learning rate is 0.001, λp = 0.1 for both method, λ1 m = λ2 m = 0.1 for our generator objective. We use Adam optimizer with learning rate lr = 0.001, and the exponent decay rate of first moment β1 = 0.8. The parameters of our model are: λp = 0.1, λ1 m = λ2 m = 0.1. The learning rate is decayed every 10K steps with a base of 0.99. The mini-batch size is 128. The training stops after 500 epochs. Our default parameters are used for all experiments λp = 1.0, λr = 1.0, λ1 m = λ2 m = 1.0. Learning rate, β1, β2 for Adam is (lr = 0.0002, β1 = 0.5, β2 = 0.9). The generator is trained with 350K updates for logarithm loss version (Eq. 7) and 200K for hinge loss version (Eq. 8) to converge better. |