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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811654.pdf · 2019-04-04 · Using preprocessing code provided by Kuleshov et al.'s GitHub repositoryl , I generated
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15806104.pdf · Description 3 inputs, 1 hidden layer, 100 units 3 inputs, 1 hidden layer, 100 units 4 inputs, 1
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15773386.pdf · camera at any given position and orientation. A random sampling of camera positions is taken within
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812583.pdfwith 1,716 car models. The full car images are labeled with bounding boxes and viewpoints. Each car model
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15782825.pdf · Generative Adversarial Networks (GANs) [Goodfellow et al, 2014; Isola et al, 2017] and Variational
cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811350.pdf · Monet painting to photo task. We gather Monet paintings both from the internet and from Wikiart.org.
CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681615.pdfStanford University 1050 Arastradero Rd., Stanford, CA kkaganov [ at ] stanford.edu Abstract In order
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15811843.pdfincluding flipping, cropping, rotating, and etc. MOM Figure 1. Sample image of Bart Simpson, Homer Simpson