Bayesian GAN
Authors: Yunus Saatci, Andrew G. Wilson
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed Bayesian GAN (henceforth titled Bayes GAN) on six benchmarks (synthetic, MNIST, CIFAR-10, SVHN, and Celeb A) each with four different numbers of labelled examples. We consider multiple alternatives, including: the DCGAN [9], the recent Wasserstein GAN (W-DCGAN) [1], an ensemble of ten DCGANs (DCGAN-10) which are formed by 10 random subsets 80% the size of the training set, and a fully supervised convolutional neural network. |
| Researcher Affiliation | Collaboration | Yunus Saatchi Uber AI Labs Andrew Gordon Wilson Cornell University |
| Pseudocode | Yes | Algorithm 1 One iteration of sampling for the Bayesian GAN. |
| Open Source Code | Yes | We have made code and tutorials available at https://github.com/andrewgordonwilson/bayesgan. |
| Open Datasets | Yes | We evaluate our proposed Bayesian GAN (henceforth titled Bayes GAN) on six benchmarks (synthetic, MNIST, CIFAR-10, SVHN, and Celeb A)... MNIST is a well-understood benchmark dataset consisting of 60k (50k train, 10k test) labeled images of hand-written digits. CIFAR-10 is also a popular benchmark dataset [7], with 50k training and 10k test images... The Street View House Numbers (SVHN) dataset... The large Celeb A dataset contains 120k celebrity faces... |
| Dataset Splits | Yes | MNIST is a well-understood benchmark dataset consisting of 60k (50k train, 10k test) labeled images of hand-written digits. CIFAR-10 is also a popular benchmark dataset [7], with 50k training and 10k test images... Standard train/test splits are used for MNIST, CIFAR-10 and SVHN. For Celeb A we use a test set of size 10k. |
| Hardware Specification | Yes | All experiments were performed on a single Titan X GPU for consistency, but Bayes GAN and DCGAN-10 could be sped up to approximately the same runtime as DCGAN through multi-GPU parallelization. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) were explicitly stated for reproducibility. |
| Experiment Setup | Yes | For the Bayesian GAN we place a N(0, 10I) prior on both the generator and discriminator weights and approximately integrate out z using simple Monte Carlo samples. We run Algorithm 1 for 5000 iterations and then collect weight samples every 1000 iterations and record out-of-sample predictive accuracy using Bayesian model averaging (see Eq. 5). For Algorithm 1 we set Jg = 10, Jd = 1, M = 2, and nd = ng = 64. As suggested in Appendix G of Chen et al. [3], we employed a learning rate schedule which decayed according to γ/d where d is set to the number of unique real datapoints seen so far. |