Upload File

cs230 deep learningcs230.stanford.edu/projects_fall_2018/reports/12450134.pdf · multi-object...

  • Home
  • Documents
  • 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
6

Upload: others

Post on 13-Oct-2020

2 views

Category:

Documents


0 download

Report

TRANSCRIPT

Page 1: 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
Page 2: 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
Page 3: 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
Page 4: 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
Page 5: 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
Page 6: 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

cs230.stanford.educs230.stanford.edu/projects_fall_2018/reports/12449174.pdf · YOLO ensembles performs marginally better than YOLO as a single model. In addition, some steps ofChexNet

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/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_fall_2018/reports/12450133.pdf · using PERCEPTRON, ADALINE, MADALINE and BACK-PROPAGATION models. It turns out to work fairly well

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940460.pdf · also begun exploring deep unsupervised learning methods in the healthcare setting. One example includes

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/15813329.pdf · from a 2019 Kaggle Competition*. The latest model achieved 97.2% accuracy against the test set

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940506.pdf · OCR focused on historical transcription has been rarely applied on Arabic histor- ical manuscripts

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18680161.pdf · separation (BSS) eval in particular, source signal-to-distortion ratio (SDR) and signal-to-interference

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_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches

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/15806293.pdf · Most sentiment analysis studies in the finance and accounting literature use ... Apple Inc. : ]

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/15342206.pdfNicole Kidman True Class Michael Jordan Michelle Obama Barack Obama Conclusion/Future Work 88 98 Identifying

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8271110.pdf · 2018. 9. 28. · reconstruction using e.g. template fitting. None of these methods are fully satisfactory

cs230.stanford.edu › projects_fall_2018 › reports › ... · 2019-01-16 · 2 Related Work Many work has been ... From the DCGAN loss training curve in Fig.5, we can see that

cs230.stanford.educs230.stanford.edu/projects_fall_2018/reports/12449630.pdf · OpenAI Gym's classic control tasks are less explored. This study aims to present and compare results

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18673358.pdfThe ability to synthesize subsections of large volumes of texts into a concise, summarative format will

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: Lecture 9 Deep Reinforcement Learningcs230.stanford.edu/files/Lecture9.pdf · Kian Katanforoosh, Andrew Ng, Younes Bensouda Mourri I. Motivation Human Level Control through

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678885.pdfis a treasure trove of information, including that related to virality, user sentiment, networks, and

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/posters/8284325.pdf · 2018. 9. 28. · Aditya Chander ( [email protected] ), Marina Cottrell ( [email protected] ),

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18681176.pdfreviews, whether they are movie reviews, Amazon reviews, workplace reviews is a common occurrence in

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 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_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_winter_2019/reports/15810428.pdf · Food image classification has many uses in everyday tasks, ranging from search to tagging. In our

Midterm Review - CS230 Deep Learningcs230.stanford.edu/fall2018/midterm_review.pdf · Midterm Review CS230 Fall 2018. Broadcasting. Calculating Means How would you calculate the means

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/15808904.pdf · Erick Cardenas implemented the Convolutional Baseline model, setup and managed the AWS instances

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

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: 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_fall_2018/reports/12447633.pdfParas, Ledyba, Spinarak, Venonat, Sil- coon Lugia, Mesprit, Mew, Victini, Celebi, Cresselia, Volcanion,