generative networks

generative networks trying to model the input distribution and this can be done through two main components 1- generator 2- discriminator

first we train the discriminator we give the generator noise as input and we get fake data as output and we cocatenate this fake data which will be labeled (0) with real data labeled (1) and pass them through the discriminator to train him to detect if it is real or not
after that we train the advirseal network and we freeze the discriminator parameters while doing that

gans networks

there a lot of proposed models and we will discuss some of them

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images		images
.DS_Store		.DS_Store
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README.md		README.md
cycleGAN.ipynb		cycleGAN.ipynb
pytorch_ACGAN.ipynb		pytorch_ACGAN.ipynb
pytorch_CGAN.ipynb		pytorch_CGAN.ipynb
pytorch_DCGAN.ipynb		pytorch_DCGAN.ipynb
tensorflow_CGAN.ipynb		tensorflow_CGAN.ipynb
tensorflow_DCGAN.ipynb		tensorflow_DCGAN.ipynb
tensorlfow_ACGAN.ipynb		tensorlfow_ACGAN.ipynb