Upload File

cs230 deep learningcs230.stanford.edu/projects_winter_2019/reports/15808060.pdf · 100, dropout of...

  • Home
  • Documents
  • 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
6

Upload: others

Post on 30-Jun-2020

0 views

Category:

Documents


0 download

Report

TRANSCRIPT

Page 1: 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
Page 2: 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
Page 3: 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
Page 4: 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
Page 5: 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
Page 6: 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_spring_2018/reports/8290433.pdf · Stanford University {zhaozhuo, zhiyuan8, edu Abstract Damage of building is an essential indicator

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812659.pdf · 2019. 4. 4. · mean lower IOU for the YOLO model in many cases). We have pre-processed these images

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.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802276.pdf · each artist. The resulting model attained good performance over the baseline, and provided subjectively

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,

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/15811878.pdf · striker (offensive agent) and goalie (defensive agent), we explore how agents can ... formation

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: Lecture 9 Deep Reinforcement Learningcs230.stanford.edu/spring2020/lecture9.pdfCS230: Lecture 9 Deep Reinforcement Learning Kian Katanforoosh Kian Katanforoosh I. Motivation

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]

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

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15808904.pdf · Erick Cardenas implemented the Convolutional Baseline model, setup and managed the AWS instances

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.stanford.educs230.stanford.edu/projects_winter_2019/posters/15811897.pdfdeep reinforcement learning networks to play simple cooperative games. This project utilizes a simulated

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/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/15871106.pdf5.0.2 Models Using the retrained Inception ResNet classifier, we ran several experiments to test the

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/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/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/15809694.pdfexisting previous piece of artwork in a personalized manner. In our method, we alter an existing piece

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813424.pdf · [1] Alexander Toshev and Christian Szegedy. Deeppose: Human pose estimation via deep neural networks

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15792433.pdf · supermarkets: four Safeways near Palo Alto, SF and one H-E-B in Austin, TX. Between 40 and 60 snapshots

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.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813330.pdf · According to the Federal Statistics Office, 2013, the number of newly opened insolvency proceedings

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

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/15813002.pdf · global education equity (4). CS230: Deep Learning, Winter 2019, Stanford ... To evolve beyond our

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/15813327.pdf0.024 0.022 0.020 Train 0.029 0.036 0.051 Test 0.048 0.026 0.028 Train 81.19% 87.55% 74.60% Test 85.82%

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/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two

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_winter_2019/reports/15812605.pdf · FMA( free music archive) . The GTZAN dataset consists of 1000 audio tracks each 30 seconds long