On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
Authors: Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on CIFAR-10, STL-10, and Imgae Net datasets confirm that compared to other methods, our proposed method is capable of generating images with competitive quality by utilizing spectral normalization and encouraging the slow singular value decay. |
| Researcher Affiliation | Academia | Haoming Jiang, Zhehui Chen, Minshuo Chen & Tuo Zhao School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30318, USA {jianghm, zhchen, mchen393, tourzhao}@gatech.edu Feng Liu & Dingding Wang Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton, FL 33431, USA {fliu2016, wangd}@fau.edu |
| Pseudocode | Yes | Algorithm 1 Adversarial training with Spectrum Control of Discriminator, D Initialization |
| Open Source Code | No | The paper does not provide an explicit statement of open-source code release or a link to a public repository. |
| Open Datasets | Yes | Our experiments on CIFAR-10, STL-10, and Imgae Net datasets confirm that compared to other methods, our proposed method is capable of generating images with competitive quality by utilizing spectral normalization and encouraging the slow singular value decay. |
| Dataset Splits | No | The paper mentions using CIFAR-10, STL-10, and ImageNet datasets but does not explicitly provide details about specific training, validation, and test data splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper states 'All implementations are done in Chainer' but does not specify a version number for Chainer or any other software dependencies. |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma & Ba, 2014) with the following hyperparameters: (1) ndis = 1; (2) α = 0.0002, the initial learning rate; (3) β1 = 0.5, β2 = 0.999, the first and second order momentum parameters of Adam respectively. We choose tuning parameters λ = 10 and γ = 1 in all the experiments except for the Divergence regularizer, where we pick λ = 10 and γ = 0.054. We take 100K iterations in all the experiments on CIFAR-10 and 200K iterations on STL-10 as suggested in Miyato et al. (2018). |