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For this project, we’ve focused on the application of GANs in image-related tasks, but it is worth mentioning that they are not limited to them (for example, this text application of GANs). The core idea behind a Generative Adversarial Network is to create two models and have them compete against each other in order to improve them both through competition. The first model can be considered a disc
I thought that the results from pix2pix by Isola et al. looked pretty cool and wanted to implement an adversarial net, so I ported the Torch code to Tensorflow. The single-file implementation is available as pix2pix-tensorflow on github. Here are some examples of what this thing does, from the original paper: "The Sorcerer's Stone, a rock with enormous powers, such as: lead into gold, horses into
There has been a large resurgence of interest in generative models recently (see this blog post by OpenAI for example). These are models that can learn to create data that is similar to data that we give them. The intuition behind this is that if we can get a model to write high-quality news articles for example, then it must have also learned a lot about news articles in general. Or in other word
In 2014, Ian Goodfellow and his colleagues at the University of Montreal published a stunning paper introducing the world to GANs, or generative adversarial networks. Through an innovative combination of computational graphs and game theory they showed that, given enough modeling power, two models fighting against each other would be able to co-train through plain old backpropagation. The models p
August 9, 2016 Introduction Content-aware fill is a powerful tool designers and photographers use to fill in unwanted or missing parts of images. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. There are many ways to do content-aware fill, image completion, and inpainting. In this blog post, I present Raymond Yeh and Chen Chen
This post was first published on 12/29/15, and has since been migrated to Blogger. This is a tutorial on implementing Ian Goodfellow's Generative Adversarial Nets paper in TensorFlow. Adversarial Nets are a fun little Deep Learning exercise that can be done in ~80 lines of Python code, and exposes you (the reader) to an active area of deep learning research (as of 2015): Generative Modeling! Code
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