To use, upload a photo with the #ageme command. This is beta though, so will take a while to return. The other thing this bot does (the #showage command) runs instantly.
Your model seems to produce much higher quality output. I think I played a penalty by putting the users face into latent space, which is a difficult optimization to perform.
Oh yeah! I wrote a similar algorithm and constantly suffered similar problems. Constraining the latent space can be a major pain. Its hard to visualize but I think a lot of latent features may exist in non-continuous latent "pockets", meaning that the desired direction of the latent vector is dependent on your position in space.
I´m having a blast changing all the parameters. Really uncanney valley feelings. Some women come up very attractive, looking like Jessica Alba. How can I react to a face of a person that don't even exist?
On a second thought, I don´t mean that the controls behave randomly. I understand they affect related parts of the system. If you increase "baldness" on a woman's face, it will obviously increase the "male" factor and the "gray hair" factor. I understand that this faces are being generated from a continuous space. Fascinating.
Looking like Jessica Alba is not a coincidence. This model was trained on CelebA, a dataset of Hollywood celebrity face photos. Jessica Alba is almost certainly in the training set.
It would be cool to integrate a face tracker to measure features, then you could learn the latent vectors for things like eye position and then you could drag the eyes around.
https://www.kaggle.com/summitkwan/tl-gan-demo