Tensorflow (Python) implementation of a Cycle Consistant Adverserial Network(CycleGAN) with a Convolutional Neural Network (CNN) model with Gated activations, Residual connections, dilations and PostNets.
Comments/questions are welcome! Please contact: shreyas.seshadri@aalto.fi
Last updated: 30.10.2018
Copyright (C) 2018 Shreyas Seshadri, Aalto University
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The source code must be referenced when used in a published work.
train_cycleW.py - CycleGAN implementation using WGAN loss with gradient penalty [2]
model_convNet.py - 1D CNN implementation with gated units, residual connections, potNets and dilations
[1] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” Proc. ICCV 2017, pp. 2223–2232, 2017.
[2] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of Wasserstein GANs,” in Advances in Neural In- formation Processing Systems 30, , 2017, pp. 5767–5777.