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guide/14-deep-learning/How CycleGAN Works.ipynb renamed to guide/14-deep-learning/how-cyclegan-works.ipynb

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"## Introduction\n",
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"CycleGAN is and Image to Image translation model, just like [Pix2Pix](https://developers.arcgis.com/python/guide/how-pix2pix-works/). There are some challenges in Pix2Pix is that the images should be paired which are randomly organized or unpaired and we want to convert images from one domain to another just like \n",
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"CycleGAN is and image-to-image translation model, just like [Pix2Pix](https://developers.arcgis.com/python/guide/how-pix2pix-works/). The main challenge faced in Pix2Pix model is that the data required for training should be paired i.e the images of source and target domain should be of same location, and number of images of both the domains should also be same. \n",
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"The Cycle Generative Adversarial Network, or CycleGAN, is an approach to training a deep convolutional neural network for image-to-image translation tasks. The Network learns mapping between input and output images using unpaired dataset. For Example: Generating RGB imagery from SAR, multispectral imagery from RGB, map routes from satellite imagery, etc.\n",
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