Deep Learning with R Cookbook

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Despite the stable architecture of DCGANs, convergence is still not guaranteed, and training could be unstable. There are a few architectural features and training procedures that, when applied while training GANs, show a remarkable improvement in their performance. These techniques leverage the heuristic understanding of the non-convergence problem and lead to improved learning performance and sample generation. In fact, in a few cases, the generated data cannot be distinguished from the real data for specific datasets, such as MNIST, CIFAR, and many more.

The following are a few techniques that can be used to achieve the same:

• Feature matching: This technique provides a new objective to the generator in order to generate ...