cs230.stanford.edu · 2018-09-28 · perform CNN detection independently on each Region of...

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Transcript of cs230.stanford.edu · 2018-09-28 · perform CNN detection independently on each Region of...

Page 1: cs230.stanford.edu · 2018-09-28 · perform CNN detection independently on each Region of Interest(RoI). Faster R-CNN extends this further with an initial stage that proposes candidate
Page 2: cs230.stanford.edu · 2018-09-28 · perform CNN detection independently on each Region of Interest(RoI). Faster R-CNN extends this further with an initial stage that proposes candidate
Page 3: cs230.stanford.edu · 2018-09-28 · perform CNN detection independently on each Region of Interest(RoI). Faster R-CNN extends this further with an initial stage that proposes candidate
Page 4: cs230.stanford.edu · 2018-09-28 · perform CNN detection independently on each Region of Interest(RoI). Faster R-CNN extends this further with an initial stage that proposes candidate
Page 5: cs230.stanford.edu · 2018-09-28 · perform CNN detection independently on each Region of Interest(RoI). Faster R-CNN extends this further with an initial stage that proposes candidate
Page 6: cs230.stanford.edu · 2018-09-28 · perform CNN detection independently on each Region of Interest(RoI). Faster R-CNN extends this further with an initial stage that proposes candidate

cs230.stanford.educs230.stanford.edu/projects_fall_2018/posters/12377987.pdf · U.S. Timely, accurate diagnosis is a critical factor in determining patient outcomes. Currently, pneumonia

cs230.stanford.educs230.stanford.edu/projects_fall_2018/posters/12377987.pdf · U.S. Timely, accurate diagnosis is a critical factor in determining patient outcomes. Currently, pneumonia

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289614.pdfduration, or only textual features, such as project description and keywords. To our knowledge, we are

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289614.pdfduration, or only textual features, such as project description and keywords. To our knowledge, we are

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681514.pdfFinal part:8x8x2048 1001 Auxiliary Classifier Figure 5: Original InceptionV3 Neural Network Schema(17)

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681514.pdfFinal part:8x8x2048 1001 Auxiliary Classifier Figure 5: Original InceptionV3 Neural Network Schema(17)

CS230: Lecture 5cs230.stanford.edu/fall2017/files/CS230_Handout6.pdf · CS230: Lecture 5 Advanced topics in Object Detection Kian Katanforoosh, Andrew Ng Stanford University. Kian

CS230: Lecture 5cs230.stanford.edu/fall2017/files/CS230_Handout6.pdf · CS230: Lecture 5 Advanced topics in Object Detection Kian Katanforoosh, Andrew Ng Stanford University. Kian

CS230: Lecture 5cs230.stanford.edu/files_fall2017/CS230_Handout6.pdf · Ross Girshick Jeff Donahue Trevor Darrell Jitendra Malik: ... (2014) Kian Katanforoosh, Andrew Ng R-CNN (Regions

CS230: Lecture 5cs230.stanford.edu/files_fall2017/CS230_Handout6.pdf · Ross Girshick Jeff Donahue Trevor Darrell Jitendra Malik: ... (2014) Kian Katanforoosh, Andrew Ng R-CNN (Regions

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_fall_2018/reports/12437786.pdf · ANET achieved 0.87 recall rate across all test cases. CS230: Deep Learning, Fall 2018, Stanford University,

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.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_winter_2019/reports/15813380.pdf · CS230 Final Project: Milestone Topic: Transfer Learning Ajay Sohmshetty (collaboration with Amir

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813380.pdf · CS230 Final Project: Milestone Topic: Transfer Learning Ajay Sohmshetty (collaboration with Amir

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/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_spring_2019/reports/18662951.pdffrom the logs for different layers of the software stack, and abstract from a high level (later cnn ...

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18662951.pdffrom the logs for different layers of the software stack, and abstract from a high level (later cnn ...

White Christmas: Remaking Augmented Reality Censorship ...cs230.stanford.edu/projects_fall_2019/reports/26262130.pdf · MOT 2016 dataset and trained the instance segmentation part

White Christmas: Remaking Augmented Reality Censorship ...cs230.stanford.edu/projects_fall_2019/reports/26262130.pdf · MOT 2016 dataset and trained the instance segmentation part

Real-Time Seismic Attributes Computation with Conditional …cs230.stanford.edu/projects_winter_2020/reports/32623969.pdfIn this context, seismic attributes contribute to the process,

Real-Time Seismic Attributes Computation with Conditional …cs230.stanford.edu/projects_winter_2020/reports/32623969.pdfIn this context, seismic attributes contribute to the process,

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18675538.pdfconvolutional neural networks. " Convolutional Neural Networks for Visual Recognition 2 (2016). [3] Sharma,

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18675538.pdfconvolutional neural networks. " Convolutional Neural Networks for Visual Recognition 2 (2016). [3] Sharma,

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

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.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

cs230.stanford.educs230.stanford.edu/projects_spring_2018/posters/8285590.pdf · melody. Chord arrangement involves both conventional rules and creativity. Ideal model: Generate chords

cs230.stanford.educs230.stanford.edu/projects_spring_2018/posters/8285590.pdf · melody. Chord arrangement involves both conventional rules and creativity. Ideal model: Generate chords

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

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

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_spring_2019/reports/18681615.pdfStanford University 1050 Arastradero Rd., Stanford, CA kkaganov [ at ] stanford.edu Abstract In order

cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6937153.pdf · 3.3.2 Mask RCNN Mask R-CNN is an extension of the Faster RCNN model [2]. Faster R-CNN is a Region Proposal

cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6937153.pdf · 3.3.2 Mask RCNN Mask R-CNN is an extension of the Faster RCNN model [2]. Faster R-CNN is a Region Proposal