The Numerics of GANs
Authors: Lars Mescheder, Sebastian Nowozin, Andreas Geiger
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train. |
| Researcher Affiliation | Collaboration | Lars Mescheder Autonomous Vision Group MPI Tübingen lars.mescheder@tuebingen.mpg.de Sebastian Nowozin Machine Intelligence and Perception Group Microsoft Research sebastian.nowozin@microsoft.com Andreas Geiger Autonomous Vision Group MPI Tübingen andreas.geiger@tuebingen.mpg.de |
| Pseudocode | Yes | Algorithm 1 Simultaneous Gradient Ascent (Sim GA) and Algorithm 2 Consensus optimization |
| Open Source Code | Yes | 1The code for all experiments in this paper is available under https://github.com/LMescheder/ The Numerics Of GANs. |
| Open Datasets | Yes | CIFAR-10 and Celeb A In our second experiment, we apply our method to the cifar-10 and celeb A datasets |
| Dataset Splits | No | The paper references datasets and training processes but does not provide specific details on training, validation, or test data splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions that the code is available for experiments. |
| Software Dependencies | No | The paper mentions deep learning frameworks like TensorFlow [1] and PyTorch [19] but does not specify the version numbers used in the experiments. |
| Experiment Setup | Yes | For both the generator and critic we use fully connected neural networks with 4 hidden layers and 16 hidden units in each layer. For all layers, we use RELU-nonlinearities. We use a 16-dimensional Gaussian prior for the latent code z and set up the game between the generator and critic using the utility functions as in [10]. To test our method, we run both Sim GA and our method with RMSProp and a learning rate of 10 4 for 20000 steps. For our method, we use a regularization parameter of γ = 10. [...] using a DC-GAN-like architecture [21] without batch normalization in the generator or the discriminator. For celeb A, we additionally use a constant number of filters in each layer and add additional RESNET-layers. |