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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).

Why Generative Models? 18 - Realistic samples for artwork, super-resolution, colorization, etc. - Generative models of time-series data can be used for simulation and planning (reinforcement learning applications!) - Training generative models can also enable inference of latent representations that can be useful as general features

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