ganGenerativeData

Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation and missing data completion. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) doi:10.48550/arXiv.1406.2661.

Last 30 days

This package has been downloaded 508 times in the last 30 days. Not bad! The download count is somewhere between 'small-town buzz' and 'moderate academic conference'. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 5,147 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was Oct 02, 2024 with 85 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.