Generative Networks like GANs are unique to other deep learning models in that they generate a sample instead of optimizing for an output. This allows for a measure of creativity; scientists can analyze the output of a generative model to understand a biological system.
The purpose of this article is to detail the potential applications of GANs to scientific research, so I will assume a preliminary understanding of GANs. Basically, GANs contains a generator which learns the distribution of a dataset with the help of a discriminator, resulting in a model capable of outputting new samples.
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