Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU)
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented d
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversa
Learning Deep Representations of Fine-grained Visual Descriptions
State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually encoded vectors describing shared characteristics among categories. Despite good performance, attributes have limitations: (1) finer-grained recognition requires commen
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Unsupervised Computer Vision: The Current State of the Art
Deep Style: Using Variational Auto-encoders for Image Generation
