Fast and sophisticated At their core, GANs are a marriage of two neural networks, making them twice as difficult to train as single neural networks. Once training begins, the correct combinations of hyperparameters are essential to producing high-quali- ty, accurate outputs at the end of the process. Hyperpa- rameters like the number of layers, number of neurons in each layer, learning rate, optimizer, and architectural decisions of using the right GAN variant are critical to tune to the specific real input data provided. As GANs are still in their infancy, we discovered that many data science users might need more technical knowledge of deep-learning algorithms to generate high-quality data. To address this, we consolidated our extensive research on which GAN works best for specific data sets into a utility that guides users. Acting as a “plug-and-play” code accelerator, it is designed to automatically select and use the parameters that will produce the best results based on the original input data. Using these recommended parameters generated synthetic data incredibly quickly and with good similarity to real input data.
For highly technical users who want complete control of the GAN setup, the Fractal utility provides maximum flexibility to adjust every individual parameter of both the generator and the discriminator. The ability of the Fractal model to cater to users across a wide range of technical skill sets makes it one of the most sophisticated GANs capabilities currently available. Future applications of GANs We are entering phase 3 of our development, exploring methods of generating multiple data sets in one go without compromising the inter-relationships between them. Our developments have significantly advanced Fractal’s capability in the Generative AI space. We are optimistic that our existing and prospective clients will benefit tremen - dously from this offering and further solidify Fractal’s position as a leader in AI research and solutions develop- ment in the wider analytics industry.
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