Reimplementation of research papers on Generative Adversarial Networks and cyclic Generative Adversarial Networks
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.ipynb_checkpoints
data
parameters
Cycle_GAN_cuda.ipynb
Cycle_GAN_cuda.py
GAN.ipynb
GAN_cuda.ipynb
README.md
discriminator.ipynb
double_comp.ipynb
noise_ip_Generator.ipynb
single_ip_generator.ipynb

README.md

Generative Adversarial Networks:

Reimplementation of research paper on Generative Adversarial Networks:

https://arxiv.org/pdf/1406.2661.pdf

Sample Results:

(for MNIST dataset)

final 2 results are obtained with 5 epochs of training.

Cyclic Generative Adversarial Networks:

Reimplementation of research paper on Cyclic-Generative Adversarial Networks:

https://arxiv.org/pdf/1703.10593.pdf