We sample some data points from our dataset , feed them to the concept generator which learns the concepts and sends them to the concept discriminator, which tries to predict the classes for those concepts. So, the concept discriminator loss implies how good our concept discriminator is at predicting the classes and it can be represented as follows:

Our loss function can be any loss function according to our task. For example, it can be a cross entropy loss if we're performing classification tasks.