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processes, but people tend to use neural networks for this and the generator you give it some r a ndom noise a nd t ha t’s t he r a ndom. T ha t’s w here it g et s it s sou rce of R a ndom ness so that it can give multiple answers to the same question effectively some random noise and it generates an image from that noise and t he idea is it’s supposed to look l i ke a ca t . S o t he way that we do this with a generative adversarial network is is this architecture, whereby you have 2 net work s play i ng a g a me ef fect ively. It’s a compet it ive g a me. It’s a d versa r ia l bet ween t hem. A nd i n fa ct , it’s a ver y si m i la r k i nd of game to the games we talked about in the prev iou s t he a lpha g o v ideo, r ight? It’s a m i n /ma x game because these two networks are f ighting over one number. One of them wants the number to be high one of t hem wa nts to k now. No. And what that number actually is is the error rate of t he discriminator. So T he discriminator wants a lower rate. The generator once a higher rate. The discriminators job is to look at an image, which could have come from the original data set. Or it could have come from the generator and its job is to say yes. T his is a rea l ima ge or k now, t his is fake and outputs a number between 0 and 1 like 1 483 0104. It’s fa ke for ex a mple. A nd t he generator gets fed as its input just some random noise and it then generates an image from t ha t a nd it’s rew a rd, you k now, it’s t r a i n i n g is pretty much the inverse of what the discriminator says for that image. So if it produces an image, which the discriminator can immediately tell us take it gets a negative reward, you k now, it’s t ha t’s it s t r a i ned not to do t ha t . If it manages to produce an image that the discriminator can’t tell is fa ke.

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