My implementation of cycleGAN (https://arxiv.org/pdf/1703.10593.pdf), included are some helper files. This uses Tensorflow. CycleGAN is a method used to create neural networks that can translate from one domain to another. It consists of four neural networks: two generators than transform from one domain into the other and two discriminators that try to decide between real images and fake ones.
Discriminator Architecture: This is composed of several convolutional layers with stride two, with a final fully connected layer - which is supposed to be a convolution that outputs a single number but they work out to the same thing. This is sometimes known as a patch net.
Generator Architecture: Downsampled using convolutions with stride two, followed be several residual blocks, then upsampled with transposed convolutions with some skip layers from the previous down sampled layers.
Mostly leaky relu is used in both networks. Training is done using mean squared error, I found it worked better than sigmoid cross entropy.
Using cycleGAN for Male <-> Female conversion.
The celebA dataset.
Key:
Rows 1 and 4: Original Images
Rows 2 and 5: Cycle consistency F->M->F and M->F->M
Rows 3 and 6: Domain transfer F->M and M->F
This particular project could probably use some more training, but overall I see it as a success. Male<->Female translation has been shelved for something else that I am currently working on.
The structure of the generator I used differs slightly from that of the paper, but not significantly - I just used some extra skip layers. The current uploaded cycleGAN implementation is the most recent one I am currently working with, so to get it working on celebA you will need to update some variables.