AI Guild

It's a podcast akin to grabbing coffee with Creative AI researchers. The target audience is people with technical competency. The focus will be on the Creative AI space. Our mission is to bring an open & collaborative AI to all of humanity. Obsessed with consuming the latest Creative AI research, we found ourselves stuck between two types output from the nascent industry: PhD level research or BuzzFeed level simplification. This is our attempt to deliver a hybrid solution that is consumable by people that can comprehend & some day contribute to.

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Image-to-Image Translation w/ Conditional Adversarial Nets

Updated about 14 days ago.

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!

"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:

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