Improving GAN with Neighbors Embedding and Gradient Matching
Authors: Ngoc-Trung Tran, Tuan-Anh Bui, Ngai-Man Cheung5191-5198
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |