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GTC ON-DEMAND

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Abstract:
We present a deep neural network architecture for estimating 3D depth from stereo images. The network is modeled after computer vision stereo matching pipelines to simplify training process. Our loss function consists of a photometric loss term and Lidar based loss terms. This combination makes it possible to train our DNN in a supervised, semi-supervised and completely unsupervised way. Our DNN produces depth maps that have accuracy similar to Lidar based depth. We also compare our stereo DNN architecture to other stereo architectures as well as to a monocular depth DNN architecture. We demonstrate qualitative and quantitative test results.
We present a deep neural network architecture for estimating 3D depth from stereo images. The network is modeled after computer vision stereo matching pipelines to simplify training process. Our loss function consists of a photometric loss term and Lidar based loss terms. This combination makes it possible to train our DNN in a supervised, semi-supervised and completely unsupervised way. Our DNN produces depth maps that have accuracy similar to Lidar based depth. We also compare our stereo DNN architecture to other stereo architectures as well as to a monocular depth DNN architecture. We demonstrate qualitative and quantitative test results.  Back
 
Topics:
Computer Vision, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8660
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Abstract:

This talk will cover the recently announced state-of-the-art GPU visualization and compute infrastructure in Microsoft's Azure cloud including how the GPUs are exposed using new capabilities in Hyper-V as part of Microsoft Server 2016, more importantly, the session will cover how you can leverage deep learning libraries such as CNTK on the N-Series GPUs. CNTK is an open source computational network toolkit that is designed for single GPU and Multi-GPU scenarios and is a highly flexible toolkit. This session is aimed at folks who would like to learn more about how to utilize and leverage Azure for deep learning, simulation, HPC, visualizing Open GL and DirectX applications.

This talk will cover the recently announced state-of-the-art GPU visualization and compute infrastructure in Microsoft's Azure cloud including how the GPUs are exposed using new capabilities in Hyper-V as part of Microsoft Server 2016, more importantly, the session will cover how you can leverage deep learning libraries such as CNTK on the N-Series GPUs. CNTK is an open source computational network toolkit that is designed for single GPU and Multi-GPU scenarios and is a highly flexible toolkit. This session is aimed at folks who would like to learn more about how to utilize and leverage Azure for deep learning, simulation, HPC, visualizing Open GL and DirectX applications.

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Topics:
Data Center & Cloud Infrastructure, Artificial Intelligence and Deep Learning, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6839
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Abstract:

This talk provides an overview of how Microsoft uses its open-source, distributed deep learning toolkit, CNTK, to make our products and services better. We'll show how you can use CNTK to train deep learning models of almost any topology and scale out to many GPUs. We'll review some of the challenges arising in scaling out deep learning workloads and CNTK way of solving them.

This talk provides an overview of how Microsoft uses its open-source, distributed deep learning toolkit, CNTK, to make our products and services better. We'll show how you can use CNTK to train deep learning models of almost any topology and scale out to many GPUs. We'll review some of the challenges arising in scaling out deep learning workloads and CNTK way of solving them.

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Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6843
Streaming:
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