Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Authors: Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first explore the qualitative benefits of our method on image generation tasks: MNIST dataset (Le Cun et al., 1998) of hand-written digits and the Celeb A (Liu et al., 2015) dataset of human faces. Then for more quantitative evaluation we use the CIFAR-10 dataset (Krizhevsky & Hinton, 2009) and use the Inception Score introduced in (Salimans et al., 2016). |
| Researcher Affiliation | Academia | 1Princeton University, Princeton NJ 2Duke University, Durham NC. |
| Pseudocode | No | The paper describes the MIX+GAN protocol but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Related code is public online at https://github.com/ Princeton ML/MIX-plus-GANs.git |
| Open Datasets | Yes | MNIST dataset (Le Cun et al., 1998) of hand-written digits and the Celeb A (Liu et al., 2015) dataset of human faces. Then for more quantitative evaluation we use the CIFAR-10 dataset (Krizhevsky & Hinton, 2009) |
| Dataset Splits | No | The paper mentions training on datasets but does not explicitly provide training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components and techniques (e.g., ADAM, DCGAN, WASSERSTEINGAN) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use exponentiated gradient (Kivinen & Warmuth, 1997): store the log-probabilities { ui, i 2 [T]}, and then obtain the weights by applying soft-max function on them: wui = e ui PT k=1 e uk , i 2 [T]. ... with learning rate lr = 0.0001. ... mixtures of 5 generators and 5 discriminators are used. |