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Lecture 13: Generative Models

Lecture 13: Generative Models Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 1 May 18, 2017. Administrative Midterm grades released on Gradescope this week A3 due next Friday, 5/26. HyperQuest deadline extended to Sunday 5/21, 11:59pm Poster session is June 6. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 2 May 18, 2017. Overview Unsupervised Learning Generative Models PixelRNN and PixelCNN. Variational Autoencoders (VAE). Generative adversarial Networks (GAN). Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 3 May 18, 2017. Supervised vs Unsupervised Learning Supervised Learning Data: (x, y). x is data, y is label Goal: Learn a function to map x -> y Examples: Classification, regression, object detection, semantic segmentation, image captioning, etc. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 4 May 18, 2017. Supervised vs Unsupervised Learning Supervised Learning Data: (x, y).

Generative models Explicit density Implicit density Direct Tractable density Approximate density Markov Chain Variational Markov Chain Variational Autoencoder Boltzmann Machine GSN GAN Figure copyright and adapted from Ian Goodfellow, Tutorial on Generative Adversarial Networks, 2017. Today: discuss 3 most popular types of generative models today

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