In the past few years, GANs have been a hot topic and a lot of papers are being published every year regarding GANs. But I always see that either the results of the generator are being shown (sample pictures or anything else generated by the generator) or the feature embeddings are used for other purposes. In some cases, first the GAN is trained with unsupervised data and then the discriminator network is trained with supervised data to predict the output.
What I've never quite seen properly is the use of discriminator to predict a given output. Suppose I have a dataset with points (with N features) of only true class label and I am interested in a sort of one class classification problem. It is not feasible to collect data of the false class label. I am interested to train a GAN with the data points (true class) I have and the generator will obviously generate random samples and so on. So after the GAN is trained, given a new point I want the discriminator to predict whether that point belongs to true class or not.
- Is there anything wrong with this approach?
- I can't find any decent paper that uses this sort of approach. If anyone knows of any such paper then please provide it in your answer.
- Feel free to explain your thoughts on the pros and cons of this approach.