GANs have certain advantages over other methods of supervised or unsupervised learning:
- GANs are an unsupervised learning method: Acquiring labeled data is a manual process that takes a lot of time. GANs don't require labeled data; they can be trained using unlabeled data as they learn the internal representations of the data.
- GANs generate data: One of the best things about GANs is that they generate data that is similar to real data. Because of this, they have many different uses in the real world. They can generate images, text, audio, and video that is indistinguishable from real data. Images generated by GANs have applications in marketing, e-commerce, games, advertisements, and many other industries.
- GANs learn density distributions of data: GANs learn the internal representations of data. As mentioned earlier, GANs can learn messy and complicated distributions of data. This can be used for many machine learning problems.
- The trained discriminator is a classifier: After training, we get a discriminator and a generator. The discriminator network is a classifier and can be used to classify objects.