Mixed batches and symmetric discriminators for GAN training
Authors: Thomas LUCAS, Corentin Tallec, Yann Ollivier, Jakob Verbeek
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, our approach reduces mode collapse in GANs on two synthetic datasets, and obtains good results on the CIFAR10 and Celeb A datasets, both qualitatively and quantitatively. |
| Researcher Affiliation | Collaboration | 1Universit e Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France. 2Universit e Paris Sud, INRIA, equipe TAU, Gif-sur-Yvette, 91190, France. 3Facebook Artificial Intelligence Research Paris, France. |
| Pseudocode | No | The paper describes the architecture and methods in text and figures but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it include a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | Experimentally, our approach reduces mode collapse in GANs on two synthetic datasets, and obtains good results on the CIFAR10 and Celeb A datasets, both qualitatively and quantitatively. and The synthetic dataset from Zhang et al. (2017) is explicitly designed to test mode dropping. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions optimizers and network types but does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The models are trained on their respective losses using the Adam (Kingma & Ba, 2015) optimizer, with default parameters. The discriminator is trained for five steps for each generator step. and The same Adam hyperparameters from (Miyato et al., 2018) are used for all models: α = 2e 4, β1 = 0.5, β2 = 0.999, and no learning rate decay. We performed hyperparameter search for the number of discrimination steps between each generation step, ndisc, over the range {1, . . . , 5}, and for the batch smoothing parameter γ over [0.2, 0.5]. All models are trained for 400,000 iterations, counting both generation and discrimination steps. |