The course slides:

Generative models

Explicit

What kind of distribution could have generated this data?

  • We can learn an explicit model of the data, this

  • Answer quesitons using the model

Impilicit

simulator that cna generate same properties

In ML, we train the models to maximize the probability distribution of the data under our model.

train latent variable models with maximum lax gets tricky use approxiate maximum likelihood

2014 GAN Ppaer show we can go from simple image of digits to images of faces, small resolution

black and white to colored

break the imagenet barrier in 2018, trained

generate faces at very high resolution quite photo realistic

not only on high diversity but also high resolution

how are gans able to learn the probabilic so accuaraly to generate such data

player:

  • discriminator
  • generator

have a distribution on the input of our model

variational auto-encoder

distribution - multi-variate gaussian noise

noise is much more dimensional than the data

pass it to deterministic nn

discriminator given some set of samples from our data and given form models,

are these real or are t hese generated?

discriminator as a teacher guide and also improve itself

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