GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:

The session is focused on applying deep learning to predictive maintenance for paper manufacturing. Although addressing this problem with machine learning methods requires considerable training data that's usually not available in the paper industry, we'll introduce an approach that combines GANs and reinforcement learning. We'll describe how that makes it possible to create a digital twin of equipment based on data from sensors, which works like a virtual environment for synthetic data generation.

The session is focused on applying deep learning to predictive maintenance for paper manufacturing. Although addressing this problem with machine learning methods requires considerable training data that's usually not available in the paper industry, we'll introduce an approach that combines GANs and reinforcement learning. We'll describe how that makes it possible to create a digital twin of equipment based on data from sensors, which works like a virtual environment for synthetic data generation.

  Back
 
Topics:
Consumer Engagement & Personalization, AI Application, Deployment & Inference, Intelligent Machines, IoT & Robotics, Industrial Inspection
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9640
Streaming:
Download:
Share:
 
Abstract:
We'll discuss DNN applications for determination of main facial skin biomarkers using a face photo. While there are a lot of other factors that enable to determine human age with high accuracy, the most obvious factor is how your face looks. Tracking face wrinkles enables us to track not only skin ageing process as such, but also the results and efficiency of treatment used. By following the dynamics of wrinkles appearance, it is possible to find out which treatment is more suitable for a particular face or skin type and hence provide recommendations.
We'll discuss DNN applications for determination of main facial skin biomarkers using a face photo. While there are a lot of other factors that enable to determine human age with high accuracy, the most obvious factor is how your face looks. Tracking face wrinkles enables us to track not only skin ageing process as such, but also the results and efficiency of treatment used. By following the dynamics of wrinkles appearance, it is possible to find out which treatment is more suitable for a particular face or skin type and hence provide recommendations.  Back
 
Topics:
Artificial Intelligence and Deep Learning, Press-Suggested Sessions: AI & Deep Learning, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6272
Streaming:
Download:
Share:
 
 
Previous
  • Amazon Web Services
  • IBM
  • Cisco
  • Dell EMC
  • Hewlett Packard Enterprise
  • Inspur
  • Lenovo
  • SenseTime
  • Supermicro Computers
  • Synnex
  • Autodesk
  • HP
  • Linear Technology
  • MSI Computer Corp.
  • OPTIS
  • PNY
  • SK Hynix
  • vmware
  • Abaco Systems
  • Acceleware Ltd.
  • ASUSTeK COMPUTER INC
  • Cray Inc.
  • Exxact Corporation
  • Flanders - Belgium
  • Google Cloud
  • HTC VIVE
  • Liqid
  • MapD
  • Penguin Computing
  • SAP
  • Sugon
  • Twitter
Next