An Introduction to Generative Models

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#Self-supervised Learning #Generative Model #Basics

Discriminative model:

  • The conditional probability of class label on data (posterior) $p(C_k\mid x)$

Generative models:

  • Likelihood $p(x\mid C_k)$
  • Sample from the likelihood to generate data
  • With latent variables $z$ and some neural network parameters $\theta$: $P(x,z\mid \theta) = p(x\mid z, \theta)p(z)$

Planted: by ;

References:
  1. Liu X, Zhang F, Hou Z, Wang Z, Mian L, Zhang J, et al. Self-supervised Learning: Generative or Contrastive. arXiv [cs.LG]. 2020. Available: http://arxiv.org/abs/2006.08218
Dynamic Backlinks to wiki/machine-learning/generative-models/generative:
Variational Auto-Encoder
In an inference problem, $p(z\vert x)$, which is used to infer $z$ from $x$. $$ p(z\vert x) = …

L Ma (2021). 'An Introduction to Generative Models', Datumorphism, 08 April. Available at: https://datumorphism.leima.is/wiki/machine-learning/generative-models/generative/.