J
Jonathon Shlens
Researcher at Google
Publications - 116
Citations - 88633
Jonathon Shlens is an academic researcher from Google. The author has contributed to research in topics: Object detection & Artificial neural network. The author has an hindex of 53, co-authored 116 publications receiving 63492 citations. Previous affiliations of Jonathon Shlens include Salk Institute for Biological Studies.
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Pixel Recursive Super Resolution
TL;DR: In this article, a pixel recursive super resolution model is proposed to synthesize realistic details into images while enhancing their resolution, which is able to represent a multimodal conditional distribution by properly modeling the statistical dependencies among the high resolution image pixels, conditioned on a low resolution input.
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Using Videos to Evaluate Image Model Robustness
TL;DR: This paper presents the first study of image model robustness to the minute transformations found across video frames, which it is shown that more accurate models are more robust to natural transformations, and that robusts to synthetic color distortions is a good proxy for natural robustness.
Proceedings Article
Deep Networks With Large Output Spaces
TL;DR: In this paper, a fast locality-sensitive hashing technique is proposed to approximate the actual dot product of neural networks, enabling them to scale up the training and inference to millions of output classes.
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PixColor: Pixel Recursive Colorization
TL;DR: In this paper, the task of automated colorization is relatively easy given a low-resolution version of the color image, and a conditional PixelCNN is trained to generate a low resolution color for a given grayscale image.
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A Tutorial on Independent Component Analysis
TL;DR: This tutorial provides an introduction to ICA based on linear algebra formulating an intuition for ICA from first principles so that one might learn the motivation behind ICA, learn why and when to apply this technique and in the process gain an introduction the field of active research.