SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC On-Demand

Presentation
Media
Abstract:
We'll introduce the basic concept of domain adaptation and how to use adversarial training to achieve unsupervised domain adaptation. We'll then describe how the technique is used in two tasks: improving semantic segmentation across cities, and transferring language style for image captioning. In particular, we combine domain adaptation with policy gradient-based reinforcement learning approach to transfer language style. The details and results of both tasks are published in ICCV 2017.
We'll introduce the basic concept of domain adaptation and how to use adversarial training to achieve unsupervised domain adaptation. We'll then describe how the technique is used in two tasks: improving semantic segmentation across cities, and transferring language style for image captioning. In particular, we combine domain adaptation with policy gradient-based reinforcement learning approach to transfer language style. The details and results of both tasks are published in ICCV 2017.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8200
Streaming:
Share:
 
Abstract:
We'll share experiences of end-to-end deep learning optimization on Alibaba's platform of artificial intelligence (PAI), including both offline training and online inference. For offline training, dedicated optimization is made for local and distributed environment. For online inference, the optimization is done through both algorithm and system perspectives. Both the methodology and benchmark number are shared during this session. We'll share several business applications driven by these optimizations to ensure learning to bridge the gap between low-level optimization and real business scenarios.
We'll share experiences of end-to-end deep learning optimization on Alibaba's platform of artificial intelligence (PAI), including both offline training and online inference. For offline training, dedicated optimization is made for local and distributed environment. For online inference, the optimization is done through both algorithm and system perspectives. Both the methodology and benchmark number are shared during this session. We'll share several business applications driven by these optimizations to ensure learning to bridge the gap between low-level optimization and real business scenarios.  Back
 
Topics:
Deep Learning and AI Frameworks, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8113
Streaming:
Share: