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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two

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Page 1: CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two
Page 2: CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two
Page 3: CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two
Page 4: CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two
Page 5: CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two
Page 6: CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15782416.pdf · analysis of the models results can be found in the Discussion section. ... and a recognizing textual

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15782416.pdf · analysis of the models results can be found in the Discussion section. ... and a recognizing textual

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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.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

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812313.pdf · As you can see, Trump. In this case, our mask will be an array of ai this mask turns off vectors

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812313.pdf · As you can see, Trump. In this case, our mask will be an array of ai this mask turns off vectors

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15342206.pdfNicole Kidman True Class Michael Jordan Michelle Obama Barack Obama Conclusion/Future Work 88 98 Identifying

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15342206.pdfNicole Kidman True Class Michael Jordan Michelle Obama Barack Obama Conclusion/Future Work 88 98 Identifying

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813450.pdfInput (current flight) LSTM seq Dense (x5) Prediction Figure 2: LSTM + CNN Architecture Due to the class

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813450.pdfInput (current flight) LSTM seq Dense (x5) Prediction Figure 2: LSTM + CNN Architecture Due to the class

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812470.pdf · upon them by pursuing deep learning techniques. Using techniques like LSTMs, RNNs, and highway networks,

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812470.pdf · upon them by pursuing deep learning techniques. Using techniques like LSTMs, RNNs, and highway networks,

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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question

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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/15812583.pdfwith 1,716 car models. The full car images are labeled with bounding boxes and viewpoints. Each car model

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Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15810323.pdfNetworks and Siamese Neural Networks Marios Andreas Galanis, Vladimir Kozlow ... There is a very large body

Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15810323.pdfNetworks and Siamese Neural Networks Marios Andreas Galanis, Vladimir Kozlow ... There is a very large body

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813172.pdf80/10/10 train, dev, and test splits based on recom- Fig. 1) The Starry Night by Vincent van Gogh [1]

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813172.pdf80/10/10 train, dev, and test splits based on recom- Fig. 1) The Starry Night by Vincent van Gogh [1]

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15871106.pdf5.0.2 Models Using the retrained Inception ResNet classifier, we ran several experiments to test the

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15871106.pdf5.0.2 Models Using the retrained Inception ResNet classifier, we ran several experiments to test the

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813120.pdf · animated images and applied to images earlier in the creative process. Style images from animated

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813120.pdf · animated images and applied to images earlier in the creative process. Style images from animated

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15806293.pdf · Most sentiment analysis studies in the finance and accounting literature use ... Apple Inc. : ]

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15806293.pdf · Most sentiment analysis studies in the finance and accounting literature use ... Apple Inc. : ]

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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.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

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15791197.pdf3.1 Manga The manga dataset is a subset of Manga109 [9], [8]. Manga109 consists of manga pages from 109

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15791197.pdf3.1 Manga The manga dataset is a subset of Manga109 [9], [8]. Manga109 consists of manga pages from 109

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15808060.pdf · 100, dropout of 0.2 and number of epoch 50. We train the model with Adam optimizer of learning rate

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15808060.pdf · 100, dropout of 0.2 and number of epoch 50. We train the model with Adam optimizer of learning rate

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15811993.pdfChallenge[91 on Kaggle, which presents a dataset of user submitted photos of restaurants and 9 possible

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15810428.pdf · Food image classification has many uses in everyday tasks, ranging from search to tagging. In our

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15810428.pdf · Food image classification has many uses in everyday tasks, ranging from search to tagging. In our

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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

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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811869.pdf · realistic personalised letters, formulating digital signatures, etc. In order to preserve information

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811869.pdf · realistic personalised letters, formulating digital signatures, etc. In order to preserve information

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CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6933119.pdf2 Methods: Quantum mechanics as an optimization problem Carleo [2017] outlines a theoretical formulation

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8289231.pdf · high gesture classification accuracy can be achieved using a convolutional neural network trained

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8289231.pdf · high gesture classification accuracy can be achieved using a convolutional neural network trained

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813329.pdf · from a 2019 Kaggle Competition*. The latest model achieved 97.2% accuracy against the test set

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813329.pdf · from a 2019 Kaggle Competition*. The latest model achieved 97.2% accuracy against the test set

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cs230.stanford.educs230.stanford.edu › projects_winter_2019 › posters › 15794817.pdf · on the signal of similar pixels2. Here we use the scikit-image fast-mode implementation

cs230.stanford.educs230.stanford.edu › projects_winter_2019 › posters › 15794817.pdf · on the signal of similar pixels2. Here we use the scikit-image fast-mode implementation

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812605.pdf · FMA( free music archive) . The GTZAN dataset consists of 1000 audio tracks each 30 seconds long

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812605.pdf · FMA( free music archive) . The GTZAN dataset consists of 1000 audio tracks each 30 seconds long

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Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813468.pdf · training a machine learning model. For the audio classifier presented in this work, the inputs to the

Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813468.pdf · training a machine learning model. For the audio classifier presented in this work, the inputs to the

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812222.pdf · 2019-04-04 · Iterative Cloud Point (ICP) with depth information or iterative model matching architecture

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812222.pdf · 2019-04-04 · Iterative Cloud Point (ICP) with depth information or iterative model matching architecture

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813291.pdf · The draft version of the application was written to generate entire trainset/devset up in front,

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813291.pdf · The draft version of the application was written to generate entire trainset/devset up in front,

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290634.pdf · alternative of rating food photos' attractiveness to Yelp's published approach that utilized EXIF

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290634.pdf · alternative of rating food photos' attractiveness to Yelp's published approach that utilized EXIF

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290433.pdf · Stanford University {zhaozhuo, zhiyuan8, edu Abstract Damage of building is an essential indicator

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290433.pdf · Stanford University {zhaozhuo, zhiyuan8, edu Abstract Damage of building is an essential indicator

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