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ah i'm i'm going to present i'm not sure it's adjourned recognise the provider it's cool it's gone and it's i just
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uh in a metal you or a different uh way of training out there that they share it
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so before going on in here i can i just very very with the things that i guess but
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i don't think so much about that and get out of it but i got it right
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so basically it is the only way our goal is to train and given to model
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we now what you read well which is all the discovery
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or discriminated retraining our bases are simple on the generator
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and on the real data sample from the real data distribution would you want to learn
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so basically we have implicitly bigger labels assigned one coming from
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the real beta words you are coming from the generator
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and then the train the generator that generates a in a link that this committee that the simple sound like real so
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basically it's strange because then i'll make the label because it level lock ups so because we're always this like this
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ah like it's really interesting is that the generator like in the out and go there
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uh we thought it's a mapping from what they expected to the real data space
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so these are not expect their example on some disruption within which we can easily sample
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so we can form like that like this is he saying that because there is a shot
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and i think really bad we know that the temples uh this is just an expression more than once the
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uh as you are some because we have the players one of them is trying to
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maximise the value function another one is trying to minimise the that function so basically
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uh i think maybe we're of in thing you really didn't show
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there are a lot model this division is going to be able to relate the story
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so right but uh you can interrupt the necessary so this is just to illustrate your load but may
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just uh basically this coming either it get examples from the related distribution we get in the labels
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examples from the generated the generator is getting variance from the discovery so that's how to generate a model is thing
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okay so uh this is when you generate images and balance level they found so many of digits i'm not going
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to do with it i just thought struck the size wary focusing on that but it's pretty clear uh_huh
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by the number thing but that had some big i mean that that boxing and does it grows exponentially
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so the the tray ah although it's very popular topic which are useful
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oh in many applications it's there's so many problems so there isn't
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that your top labour you really want to use that
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um so this is what we of b. b. c. it's happening but it's a lot more complicated basically
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the parameters of the discriminated the image and likeness and uh like one one
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parameter just working out the main dish and what you wanted to
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then when your devised a valid points that they shouldn't be doing this line we actually once you find them that all points
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which is going to give us the optimum the optimum else they i think
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that one but this never happens because at varying on on its use
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um and there's the neural network of bearing on what's in chains there's so many so many problems one
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of them or menu then what you want one mentioned that your slides i will just you
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all this and like this is samples examples of what collapse and it's not so basically
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uh they have been so many um variance of the garden going out with them but it's very difficult to
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say for that one and then outperforms all the rest although they have been doing someone's someone's work
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because they're they're a little higher priced and oh and depending uh said get get get different performances
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so what if i guess it uh is sewn on different mythology how we can uh can you hear me or oh
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so the parental as you probably can train a login other side of the brakes
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um and this can be applied to any variant of
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that cannot within that already exist so oh
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okay so it is motivated by you know some
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empirical observations so basically if impinges so
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uh run multiple insinuations of of of of the father stereo pairs
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we will see beneath the convergence of around the mote so you are real i'm not sure
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i can see but if you are a rerun experiment where we just they um
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oh very train one it's the one pair of other senators regularly
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and we train another one but the this one the second
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one estrangement stumble off well and symbol of well
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such adversarial paris we we didn't want to one that does give me the screen their output
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in some region of interest this is what we get the sit there
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for that generate their card it's up for this game either
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a bit of a cripple just come in there that is some uh change regularly there's what
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collapse and tons of the way that we train a a there there's no what colour
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uh the probability that a problem or it will it will not be hard right
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at the number of independent adversarial problem out here to our
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guest goes down exponentially somehow can use this uh
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so this is how the structure of the algorithm it's basically that have shaped our job with them in the end
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but there is one pair networks and uh oh which is very convenient for practical
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applications although you use many other smile appears again you then just one pair
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but here's our insensible so basically the is trained to discriminate older older
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examples and the generator is trying to pull all the display niggers
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domain we as tuned to the the diesel here's the tape all
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uh in our uh our main thing is that when when there's
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no one network things lessons them all because we didn't get
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what's collapsed or steam out okay so oh basically this is
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the essence training and can generate than just one pair
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so and it not only improves what were surprised that it not only improves our remote coverage
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but it all seemed plus converges sun rises type and you say the blood samples from
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a faint from the length examples here this is the real data distribution
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and this or samples all taken from the different local oh local uh generators
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indeed and they're not that in different colours so that little generator
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convert just in a different way than the local ones
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and they just so uh see i like that the next generations
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we see that it's like seventeen the the local and so that the trajectory of converging is different
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the best therefore the faster convergence it's is really interesting okay
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um hum some metrics when you get like a different brands like that we've done of this again
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um dark brown over other buttons on any brian you mean then when the the performance is better
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so i want to show some also it helps what collapse and uh though and i mean what we see
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on copper experiments it also happening and you know the the samples what this very interesting is that are
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just selections are also reduced and i'm i'm sure just few years
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happen
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so i asked the president bush does that big mama generator and this is the start of local ones
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so basically they're all of us like teen way more that i'm not very conceited probably the resemblance
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but there was telling you can definitely see that there ought selecting a quite a lot like about
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so uh in the blue r. square that's that will generate another you train and the other ones are regular actually
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and uh basically they are sometimes does oh like you can do this for multiple of data set
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and you say that you're due to that selection sometimes or the local paris it's going to it's going
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to completely fail the um the convergence various target level and we always get this and we also
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you mean
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even his show it i mean it's not just some parts of their comments
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to because the scene but the convergence is a lot more stable
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so basically this is what we get and this is our agenda that has been
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this is just to equalise computation although although although this
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there's more defined benefits so just you just you
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yeah

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