Introducing the Generative Adversarial Networks Specialization

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Online panel discussion on “GANs for Good” with Andrew Ng, Anima Anandkumar, Alexei Efros, Ian Goodfellow, and Sharon Zhou

Dear friends,

This special issue of The Batch celebrates the launch of our new Generative Adversarial Networks Specialization!

GANs are among the most exciting technologies to emerge from deep learning. These networks learn in a very different way than typical supervised methods for learning x-to-y mappings. By pitting a discriminator network and a generator network against one another (details below), they produce photorealistic images, medical training data, children’s book illustrations, and other types of output.

Earlier today, we held an online panel discussion on “GANs for Good” with Anima Anandkumar, Alexei Efros, Ian Goodfellow, and our course instructor Sharon Zhou. I was struck by the number of new applications GANs are enabling, and the number that are likely to come.

Ian explained that GAN-generated training examples for a particular application at Apple are one-fifth as valuable as real examples but cost much less than one-fifth as much to produce. Anima described exciting progress on disentanglement and how the ability to isolate objects in images is making it easier to control image generation (“add a pair of glasses to this face”). Alexei talked about the impact GANs are having on art through tools like Artbreeder.

All the speakers talked about alternatives to reading research papers to keep up with the exploding literature. If you missed the live discussion, you can watch a video of the entire event here.

We’re still in the early days of practical GAN applications, but I believe they will:

  • Transform photo editing and make it easier to add or subtract elements such as background objects, trees, buildings, and clouds
  • Generate special effects for media and entertainment that previously were prohibitively expensive
  • Contribute to creative products from industrial design to fine art
  • Augment datasets in small data problems in fields from autonomous driving to manufacturing

As an emerging technology, GANs have numerous untapped applications. This is a moment to dream up new ideas, because no one else may be working on them yet.

I hope this technology will spark your hunger to learn more and invent new applications that will make life better for people all over the world.

Keep learning!



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