Generative models
Supervised vs. Unsupervised
Discriminative Model vs. Generative Models vs. Conditional Generative
Discriminative: only label compete for probability mass, no competition between images
Generative: images compete with each other for probability mass
usage
Discriminative:
- Feature learning
- Assign labels to data
Generative:
- detect outliers
- feature learning
- sample to generate new data
Conditional Generative:
- assign labels while rejecting outliers
- generate new data conditioned on input labels
Autoregressive
Goal: Write down an explicit function for p(x)=f(x,W)p(x) = f(x,W)p(x)=f(x,W)
We can break down the probability function to get p(x)=p(x1,x2,...,xT)=Πt=1Tp(xt∣x1,x2,...xt−1)p(x) = p(x_1,x_2,...,x_T) = \Pi_{t=1}^Tp(x_t|x_1,x_2,...x_{t-1})p(x)=p(x1,x2,...,xT)=
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