�ݺ�ߣshows by User: ByungSooKo1

�ݺ�ߣshows by User: ByungSooKo1 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: ByungSooKo1 / Fri, 08 Oct 2021 06:23:41 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: ByungSooKo1 [ICCV2021] Learning with Memory-based Virtual Classes for Deep Metric Learning (PPT) /slideshow/iccv2021-learning-with-memorybased-virtual-classes-for-deep-metric-learning-ppt/250398051 iccv2021ppt-211008062342
This is a presentation material for the paper of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" accepted in AAAI 2021. Written by Byungsoo Ko*, Geonmo Gu*, Han-Gyu Kim (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2103.16940 - Github: https://github.com/navervision/MemVir - Presentation video: https://www.youtube.com/watch?v=s0FcLkE-ZBY]]>
This is a presentation material for the paper of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" accepted in AAAI 2021. Written by Byungsoo Ko*, Geonmo Gu*, Han-Gyu Kim (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2103.16940 - Github: https://github.com/navervision/MemVir - Presentation video: https://www.youtube.com/watch?v=s0FcLkE-ZBY]]> Fri, 08 Oct 2021 06:23:41 GMT /slideshow/iccv2021-learning-with-memorybased-virtual-classes-for-deep-metric-learning-ppt/250398051 ByungSooKo1@slideshare.net(ByungSooKo1) [ICCV2021] Learning with Memory-based Virtual Classes for Deep Metric Learning (PPT) ByungSooKo1 This is a presentation material for the paper of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" accepted in AAAI 2021. Written by Byungsoo Ko*, Geonmo Gu*, Han-Gyu Kim (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2103.16940 - Github: https://github.com/navervision/MemVir - Presentation video: https://www.youtube.com/watch?v=s0FcLkE-ZBY <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iccv2021ppt-211008062342-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a presentation material for the paper of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" accepted in AAAI 2021. Written by Byungsoo Ko*, Geonmo Gu*, Han-Gyu Kim (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2103.16940 - Github: https://github.com/navervision/MemVir - Presentation video: https://www.youtube.com/watch?v=s0FcLkE-ZBY

[ICCV2021] Learning with Memory-based Virtual Classes for Deep Metric Learning (PPT) from Byung Soo Ko

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0 Towards Light-weight and Real-time Line Segment Detection /slideshow/towards-lightweight-and-realtime-line-segment-detection/248814433 towardslight-weightandreal-timelinesegmentdetection-byungsooko-210601060145
This is a presentation material for the paper of "Towards Light-weight and Real-time Line Segment Detection" . Written by Geonmo Gu*, Byungsoo Ko*, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2106.00186 - Github: https://github.com/navervision/mlsd]]>
This is a presentation material for the paper of "Towards Light-weight and Real-time Line Segment Detection" . Written by Geonmo Gu*, Byungsoo Ko*, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2106.00186 - Github: https://github.com/navervision/mlsd]]> Tue, 01 Jun 2021 06:01:45 GMT /slideshow/towards-lightweight-and-realtime-line-segment-detection/248814433 ByungSooKo1@slideshare.net(ByungSooKo1) Towards Light-weight and Real-time Line Segment Detection ByungSooKo1 This is a presentation material for the paper of "Towards Light-weight and Real-time Line Segment Detection" . Written by Geonmo Gu*, Byungsoo Ko*, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2106.00186 - Github: https://github.com/navervision/mlsd <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/towardslight-weightandreal-timelinesegmentdetection-byungsooko-210601060145-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a presentation material for the paper of "Towards Light-weight and Real-time Line Segment Detection" . Written by Geonmo Gu*, Byungsoo Ko*, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2106.00186 - Github: https://github.com/navervision/mlsd

Towards Light-weight and Real-time Line Segment Detection from Byung Soo Ko

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0 [AAAI2021] Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning (PPT) /slideshow/aaai2021-proxy-synthesis-learning-with-synthetic-classes-for-deep-metric-learning/245297742 aaai2021proxysynthesisfullfinal-210330012215
This is a presentation material for the paper of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" accepted in AAAI 2021. Written by Geonmo Gu*, Byungsoo Ko*, Han-Gyu Kim (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2103.15454 - Github: https://github.com/navervision/proxy-synthesis - Presentation video: https://www.youtube.com/watch?v=v_KYo2Crbig]]>
This is a presentation material for the paper of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" accepted in AAAI 2021. Written by Geonmo Gu*, Byungsoo Ko*, Han-Gyu Kim (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2103.15454 - Github: https://github.com/navervision/proxy-synthesis - Presentation video: https://www.youtube.com/watch?v=v_KYo2Crbig]]> Tue, 30 Mar 2021 01:22:15 GMT /slideshow/aaai2021-proxy-synthesis-learning-with-synthetic-classes-for-deep-metric-learning/245297742 ByungSooKo1@slideshare.net(ByungSooKo1) [AAAI2021] Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning (PPT) ByungSooKo1 This is a presentation material for the paper of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" accepted in AAAI 2021. Written by Geonmo Gu*, Byungsoo Ko*, Han-Gyu Kim (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2103.15454 - Github: https://github.com/navervision/proxy-synthesis - Presentation video: https://www.youtube.com/watch?v=v_KYo2Crbig <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aaai2021proxysynthesisfullfinal-210330012215-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a presentation material for the paper of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" accepted in AAAI 2021. Written by Geonmo Gu*, Byungsoo Ko*, Han-Gyu Kim (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2103.15454 - Github: https://github.com/navervision/proxy-synthesis - Presentation video: https://www.youtube.com/watch?v=v_KYo2Crbig

[AAAI2021] Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning (PPT) from Byung Soo Ko

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0 Controlled dropout: a different dropout for improving training speed on deep neural network /slideshow/controlled-dropout-a-different-dropout-for-improving-training-speed-on-deep-neural-network/90289597 controlleddropoutsmc2017-180311031929
"Controlled Dropout" is a different dropout method which is for improving training speed on deep neural networks. Basic idea and algorithm of controlled dropout are based on the paper "Controlled Dropout: a Different Dropout for Improving Training Speed on Deep Neural Network" which was presented in IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017.]]>
"Controlled Dropout" is a different dropout method which is for improving training speed on deep neural networks. Basic idea and algorithm of controlled dropout are based on the paper "Controlled Dropout: a Different Dropout for Improving Training Speed on Deep Neural Network" which was presented in IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017.]]> Sun, 11 Mar 2018 03:19:29 GMT /slideshow/controlled-dropout-a-different-dropout-for-improving-training-speed-on-deep-neural-network/90289597 ByungSooKo1@slideshare.net(ByungSooKo1) Controlled dropout: a different dropout for improving training speed on deep neural network ByungSooKo1 "Controlled Dropout" is a different dropout method which is for improving training speed on deep neural networks. Basic idea and algorithm of controlled dropout are based on the paper "Controlled Dropout: a Different Dropout for Improving Training Speed on Deep Neural Network" which was presented in IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/controlleddropoutsmc2017-180311031929-thumbnail.jpg?width=120&height=120&fit=bounds" /> "Controlled Dropout" is a different dropout method which is for improving training speed on deep neural networks. Basic idea and algorithm of controlled dropout are based on the paper "Controlled Dropout: a Different Dropout for Improving Training Speed on Deep Neural Network" which was presented in IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017.

Controlled dropout: a different dropout for improving training speed on deep neural network from Byung Soo Ko

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0 Neural network based autonomous navigation for a homecare mobile robot /slideshow/neural-network-based-autonomous-navigation-for-a-homecare-mobile-robot/90289040 neuralnetwork-basedautonomousnavigationforahomecaremobilerobot-180311030540
This is a presentation material of a paper "Neural Network-based Autonomous Navigation for a Homecare Mobile Robot"(http://ieeexplore.ieee.org/document/7881744/) which was presented in BigData4Healthcare 2017 (workshop in IEEE BigComp 2017).]]>
This is a presentation material of a paper "Neural Network-based Autonomous Navigation for a Homecare Mobile Robot"(http://ieeexplore.ieee.org/document/7881744/) which was presented in BigData4Healthcare 2017 (workshop in IEEE BigComp 2017).]]> Sun, 11 Mar 2018 03:05:40 GMT /slideshow/neural-network-based-autonomous-navigation-for-a-homecare-mobile-robot/90289040 ByungSooKo1@slideshare.net(ByungSooKo1) Neural network based autonomous navigation for a homecare mobile robot ByungSooKo1 This is a presentation material of a paper "Neural Network-based Autonomous Navigation for a Homecare Mobile Robot"(http://ieeexplore.ieee.org/document/7881744/) which was presented in BigData4Healthcare 2017 (workshop in IEEE BigComp 2017). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/neuralnetwork-basedautonomousnavigationforahomecaremobilerobot-180311030540-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a presentation material of a paper "Neural Network-based Autonomous Navigation for a Homecare Mobile Robot"(http://ieeexplore.ieee.org/document/7881744/) which was presented in BigData4Healthcare 2017 (workshop in IEEE BigComp 2017).

Neural network based autonomous navigation for a homecare mobile robot from Byung Soo Ko

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/iccv2021ppt-211008062342-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/iccv2021-learning-with-memorybased-virtual-classes-for-deep-metric-learning-ppt/250398051 [ICCV2021] Learning wi... https://cdn.slidesharecdn.com/ss_thumbnails/towardslight-weightandreal-timelinesegmentdetection-byungsooko-210601060145-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/towards-lightweight-and-realtime-line-segment-detection/248814433 Towards Light-weight a... https://cdn.slidesharecdn.com/ss_thumbnails/aaai2021proxysynthesisfullfinal-210330012215-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/aaai2021-proxy-synthesis-learning-with-synthetic-classes-for-deep-metric-learning/245297742 [AAAI2021] Proxy Synth...