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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.

Fully visible belief network Use chain rule to decompose likelihood of an image x into product of 1-d distributions: Explicit density model Likelihood of image x Probability of i’th pixel value given all previous pixels Will need to define ordering of “previous pixels” Complex distribution over pixel values => Express using a neural network!

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