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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).

Lecture 13 - 33 May 18, 2017 PixelRNN and PixelCNN Improving PixelCNN performance - Gated convolutional layers - Short-cut connections - Discretized logistic loss - Multi-scale - Training tricks - Etc… See - Van der Oord et al. NIPS 2016 - Salimans et al. 2017 (PixelCNN++) Pros: - Can explicitly compute likelihood p(x) - Explicit likelihood ...

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