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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.

Unsupervised Learning Data: x Just data, no labels! Goal: Learn some underlying hidden structure of the data Examples: Clustering, dimensionality reduction, feature learning, density estimation, etc. Supervised vs Unsupervised Learning Principal Component Analysis (Dimensionality reduction) This image from Matthias Scholz is CC0 public domain 3 ...

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