Advanced Deep Learning with Keras

Chapter 6. Disentangled Representation GANs

As we've explored, GANs can generate meaningful outputs by learning the data distribution. However, there was no control over the attributes of the outputs generated. Some variations of GANs like Conditional GAN (CGAN) and Auxiliary Classifier GAN (ACGAN), as discussed in the previous chapter are able to train a generator that is conditioned to synthesize specific outputs. For example, both CGAN and ACGAN can induce the generator to produce a specific MNIST digit. This is achieved by using both a 100-dim noise code and the corresponding one-hot label as inputs. However, other than the one-hot label, we have no other ways to control the properties of generated outputs.

Note

For a review on CGAN and ACGAN, ...