Christian Ledig, Lucas Theis, Ferenc Huszar, Jose ... · Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc
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arXiv:1809.07294v4 [cs.CV] 4 Sep 2019 · 2019-09-05 · super-resolution (Ledig et al.,2017), and image-to-image trans-lation (Zhu et al.,2017a). Unlike deep learning which has its
Doornhoek 910 JQ Ptn 1 - jaws.co.za · Ledig 909 JQ Ptn 7 Ledig 909 JQ Ptn 6 Doornhoek 910 JQ Ptn 1 Doornhoek 910 JQ Ptn 3 5 3 1 2 4a4b 27°5'0"E 27°5'0"E 25°20'0"S 25°20'0"S SUN
SCOPING REPORT FOR THE ENVIRONMENTAL APPLICATIONS … · SCOPING REPORT FOR THE ENVIRONMENTAL APPLICATIONS FOR CHANGES TO INFRASTRUCTURE AT BAKUBUNG PLATINUM MINE, LEDIG, NORTH WEST
Christian Ledig, Lucas Theis, Ferenc Husz´ar, Jose ... · Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew … · 2016-11-22 · the works by Johnson et al. [31] and Bruna et al. [4], who rely on a loss function closer
PDT20-12471 250 m · nu el e n e nt ier ava n t d e c omm en c r G li ve de ze gi ds vol ledig d oor t l ze y vo or d at u be gi n t Po rf avor, est e m an ... dat de batterij bijna
C/'F7' /f~ //ff West Coast Tree Improvement Programs: a ...€¦ · West Coast Tree Improvement Programs: a break-even, cost-benefitanalysis Research Paper PSW·156 F. Thomas Ledig
CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12450134.pdf · Multi-object detection is the task of finding objects in an image or video frame, which consists of
Bexell Kraemer 060120 - liu.diva-portal.orgliu.diva-portal.org/smash/get/diva2:21956/FULLTEXT01.pdf · Vid ledig kapacitet måste företag ha klart för sig hur de ska hantera kostnaden
PhysGAN: Generating Physical-World-Resilient … › pdf › 1907.04449.pdfadversarial examples, which add small-magnitude pertur-bations to inputs to evaluate the robustness of DNN-driven
CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches.
Patch-based Evaluation of Image Segmentation Evaluation of Image Segmentation Christian Ledig Wenzhe Shi Wenjia Bai Daniel Rueckert Department of Computing, Imperial College London
Markierungsexperimente an der Zweigestreiften Quelljungfer ......Bei den Kartierungen früherer Jahre wurde nicht markiert, hier erfolgte ledig lich eine qualitative Bestandserfassung.
arXiv:1803.09375v2 [cs.CV] 12 Apr 2018 · (Ledig et al.,2016), text2image (Reed et al.,2016) and image-to-image transla-tion (Isola et al.,2016) (See Figure1for examples). GANs succeed
ENGLISH-TAUGHT BACHELOR’S PROGRAMMES...Parnassos Universiteits-museum Ledig Erf t l De Inktpot City centre Library Sports Centre Olympos Utrecht Science Park Library 2.2 km = 7 min.
Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12418781.pdf · 2019-01-16 · Figure 1: Prevalence of Different Road Objects in the BDD-IOOK Dataset. In order to implement
CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12437786.pdf · ANET achieved 0.87 recall rate across all test cases. CS230: Deep Learning, Fall 2018, Stanford University,
![Page 1: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12449264.pdfadversarial network (GAN) based off of the SRGAN network developed by Ledig et. al [2]. We modified this](https://reader035.documents.pub/reader035/viewer/2022071107/5fe1456ac56c1f05f17bf45a/html5/thumbnails/1.jpg)
![Page 2: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12449264.pdfadversarial network (GAN) based off of the SRGAN network developed by Ledig et. al [2]. We modified this](https://reader035.documents.pub/reader035/viewer/2022071107/5fe1456ac56c1f05f17bf45a/html5/thumbnails/2.jpg)
![Page 3: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12449264.pdfadversarial network (GAN) based off of the SRGAN network developed by Ledig et. al [2]. We modified this](https://reader035.documents.pub/reader035/viewer/2022071107/5fe1456ac56c1f05f17bf45a/html5/thumbnails/3.jpg)
![Page 4: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12449264.pdfadversarial network (GAN) based off of the SRGAN network developed by Ledig et. al [2]. We modified this](https://reader035.documents.pub/reader035/viewer/2022071107/5fe1456ac56c1f05f17bf45a/html5/thumbnails/4.jpg)
![Page 5: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12449264.pdfadversarial network (GAN) based off of the SRGAN network developed by Ledig et. al [2]. We modified this](https://reader035.documents.pub/reader035/viewer/2022071107/5fe1456ac56c1f05f17bf45a/html5/thumbnails/5.jpg)
![Page 6: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12449264.pdfadversarial network (GAN) based off of the SRGAN network developed by Ledig et. al [2]. We modified this](https://reader035.documents.pub/reader035/viewer/2022071107/5fe1456ac56c1f05f17bf45a/html5/thumbnails/6.jpg)
![arXiv:1809.07294v4 [cs.CV] 4 Sep 2019 · 2019-09-05 · super-resolution (Ledig et al.,2017), and image-to-image trans-lation (Zhu et al.,2017a). Unlike deep learning which has its](https://static.documents.pub/doc/80x56/5f9828bbfd54a847d8714aa5/arxiv180907294v4-cscv-4-sep-2019-2019-09-05-super-resolution-ledig-et-al2017.jpg)
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![arXiv:1803.09375v2 [cs.CV] 12 Apr 2018 · (Ledig et al.,2016), text2image (Reed et al.,2016) and image-to-image transla-tion (Isola et al.,2016) (See Figure1for examples). GANs succeed](https://static.documents.pub/doc/80x56/5f3885cc8ca9c8307b10ccad/arxiv180309375v2-cscv-12-apr-2018-ledig-et-al2016-text2image-reed-et.jpg)
