My favorite thing about GANs is that, through their interactive nature, they are one of the first strategies showing how multiple models can work together in a broader system. Strategies like this provide a sneak peak at the future of ML.
Thanks for sharing!
A GAN uses a ML Model (the validator) to help train another ML Model (the producer), commonly in image generation. So the producer creates images, and the validator identifies if they are real or fake. Over time the producer gets better at its task. Def give this one a listen.
A recent article on @Forbes titled "Our Entire AI Revolution Is Built On A Correlation House Of Cards" sounds really scary, but are things really that bad? (spoiler alert... nope, they aren't) #enterpriseai#machinelearning#explainableai
https://t.co/09YDHauwsT