Image-to-Image Translation w/ Conditional Adversarial Nets

In this week's episode I chat with Philip. He worked on Image-to-Image Translation with Conditional Adversarial Networks. For this episode, I'd highly recommend you check out the YouTube version. You'll see a few different examples such as taking machine vision from something like an autonomous car and translating that to a more real image. Another example of the same model is converting a day photo to a night photo, translating a satellite aerial photo into Google Map esque design and what Philip calls "Edges to Photos" which is sort of like making a simple drawing and having the model turn into a somewhat realistic photo. The applications of this type of work are pretty wide spread. Let's hop into the show!

https://youtu.be/B1bMMF8miN8

"We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either." — Abstract from the paper.

Check out their paper here: https://arxiv.org/pdf/1611.07004.pdf

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