Metropolis-Hastings Generative Adversarial Networks
Authors: Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the benefits of the improved generator on multiple benchmark datasets, including CIFAR10 and Celeb A, using the DCGAN, WGAN, and progressive GAN. ... Results on real data (CIFAR-10 and Celeb A) and extending common GAN models (DCGAN, WGAN, and progressive GAN) are shown in Section 5. |
| Researcher Affiliation | Industry | Ryan Turner 1 Jane Hung 1 Eric Frank 1 Yunus Saatci 1 Jason Yosinski 1 1Uber AI Labs. Correspondence to: Ryan Turner <ryan.turner@uber.com>. |
| Pseudocode | Yes | Algorithm 1 MH-GAN: Input: generator G, calibrated disc. D, real samples Assign random real sample x0 to x for k = 1 to K do Draw x from G Draw U from Uniform(0, 1) if U (D(x) 1 1)/(D(x ) 1 1) then end if end for If x is still real sample x0 restart with draw from G as x0 Output: sample x from G |
| Open Source Code | Yes | 1Code found at: github.com/uber-research/metropolis-hastings-gans |
| Open Datasets | Yes | We consider the 5 5 grid of two-dimensional Gaussians used in Azadi et al. (2018)... For real data experiments we considered the Celeb A (Liu et al., 2015) and CIFAR-10 (Torralba et al., 2008) data sets... |
| Dataset Splits | Yes | We used 64,000 standardized training points and generated 10,000 points in test. ... To correct an uncalibrated classifier, denoted D X R, we use a held out calibration set (e.g., 10% of the training data) and either logistic, isotonic, or beta (Kull et al., 2017) regression to warp the output of D. |
| Hardware Specification | No | The paper mentions "Using multiple chains is also better for GPU parallelization" but does not specify any particular GPU models, CPU types, or other hardware specifications used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks, or programming languages used (e.g., PyTorch 1.x, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | Following Azadi et al. (2018), we use four fully connected layers with Re LU activations for both the generator and discriminator. The final output layer of the discriminator is a sigmoid, and no nonlinearity is applied to the final generator layer. All hidden layers have size 100, with a latent z R2. ... running MCMC to k = 640 iterations in all cases. ... Results are computed at epoch 60... |