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And they have to find a new thing that distinguishes the real from The Fakes and so on and so on in a cycle they force each ot her to i mprove a nd it’s t he sa me t h i n g here. So at the beginning the generator is ma k i n g ju st r a ndom noise ba sica l ly beca u se it’s g et t i n g r a ndom noise i n a nd it’s doi n g somet h i n g to it . W ho k nows w ha t a nd it’s A nd t he discriminating goes that looks nothing li ke a cat, you k now. A nd t hen event ua l ly because the discriminator is also not very smart at the beginning right and and they just they both get better and better the generator gets better at producing cat looking things and the discriminator gets better and better at identifying them until eventually in principle. If you run this for long enough, theoretically you end up with a situation where the generator is creating images that look exactly indistinguishable from images from the real data set. A nd t he d isc r i m i na tor i f it’s g iven a rea l i ma g e or a fake image always outputs is 0.5 5 0 /5 0 . I d o n’t k n o w c o u l d b e e i t h e r t h e s e t h i n g s are literally indistinguishable. Then you pretty much can throw away the discriminator and you’ve got a generator which you give random noise to and it outputs brand-new indistinguishable images of cats. T here’s a not her cool t h i n g a bout t h is, w h ich is every every time we ask the generator to generate New Ima ge, we’re g iv ing it some ra ndom d a t a , r i g ht we g i ve it j u s t t h i s Ve c t or of random numbers which you can think of as being a randomly selected point in a space because you k now, if you g ive it if you g ive it ten ra ndom numbers, you know between 0 & 1 or whatever that is effectively a point in a 10 d i mensiona l spa ce a nd t he t h i n g t ha t’s cool. Is that as the generator learns its Force?

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