Adversarial Lipschitz Regularization
Authors: Dávid Terjék
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Applying ALP on the critic in WGAN (WGAN-ALP), we show state-of-the-art performance in terms of Inception Score and Fréchet Inception Distance among non-progressive growing methods trained on CIFAR-10, and competitive performance in the high-dimensional setting when applied to the critic in Progressive Growing GAN trained on Celeb A-HQ. To evaluate the performance of WGAN-ALP, we trained one on CIFAR-10, consisting of 32 32 RGB images, using the residual architecture from Gulrajani et al. (2017), implemented in Tensor Flow. |
| Researcher Affiliation | Industry | Dávid Terjék Robert Bosch Kft. Budapest, Hungary david.terjek@hu.bosch.com |
| Pseudocode | No | The paper describes the methods through mathematical formulations and textual explanations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code to reproduce the presented experiments is available at https://github.com/dterjek/adversarial_lipschitz_regularization. |
| Open Datasets | Yes | To evaluate the performance of WGAN-ALP, we trained one on CIFAR-10, consisting of 32 32 RGB images... To show that ALR works in a high-dimensional setting as well, we trained a Progressive GAN on the Celeb A-HQ dataset (Karras et al., 2018), consisting of 1024 1024 RGB images. |
| Dataset Splits | No | No explicit mention of a dedicated validation dataset split with specific sizes or percentages for the main GAN experiments in Section 4. While Section A.1 mentions a validation split for a semi-supervised learning experiment, it is not for the primary focus of the paper. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions "Tensor Flow" and "Py Torch" but does not specify their version numbers or any other software dependencies with version details. |
| Experiment Setup | Yes | Closely following Gulrajani et al. (2017), we used the Adam optimizer (Kingma and Ba, 2015) with parameters β1 = 0, β2 = 0.9 and an initial learning rate of 2 10 4 decaying linearly to 0 over 100000 iterations, training the critic for 5 steps and the generator for 1 per iteration with minibatches of size 64 (doubled for the generator). We used (17) as a loss function to optimize the critic. K = 1 was an obvious choice, and we found λ = 100 to be optimal... The hyperparameters of the approximation of radv were set to ξ = 10, Pϵ being the uniform distribution over [0.1, 10], and k = 1 power iteration. |