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it’s spot s a ny wea k ness, it w i l l focu s on that and thereby Force the learner to learn to not have that weakness anymore, like one form of adversarial training people. Sometimes do is if you have a game playing You m a k e it p l a y it s e l f a l ot of t i m e s b e c a u s e Full-time, they are trying to look for weaknesses in their opponent and exploit those wea k nesses. A nd when t hey do t hat, t hey’re forced to then improve or fix those weaknesses in themselves because their opponent is exploiting those weaknesses. So Every time the every time the system finds a strategy that is extremely good against this opponent the t he opponent w ho’s a lso t hem ha s to lea r n a way of dealing with that strategy and so on and so on so as the system gets better it forces it sel f to g et bet ter beca u se it’s cont i nuou sly having to learn how to play a better and bet ter opponent . It’s qu ite it’s qu ite eleg a nt , you k now, t his is where we get to generative adversarial networks. L et’s say you’ve g ot a net work you w a nt to L et’s say you w a nt ca t pict u res. You k n ow you w a nt t o b e a b l e t o g i v e it a b u n c h of pictures of cats and have it spit out a new picture of a cat that you’ve never seen before that looks exactly like a cat the way t hey’re degenerative adversa ria l net work work s is it’s t h is a rch itect u re w here you actually have two networks. One of the Net work ’s is t he d isc r i m i na tor w ho ha s l e s s s p e l l i n g. Ye a h . I l i k e t h a t . T h e d i s c r i m i n a t or net work is a classif ier, right? So straightforward classifier you give it an image and it outputs a number between 0 and 1 and your training that in standard supervised lea r n i ng way. T hen you have a A nd t he generator Is usually a convolutional neural network. A lt hou gh act ua l ly, bot h of t hese ca n be ot her

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