An Overview of Transfer Learning

author: Sinno Jialin Pan, Institute for Infocomm Research
published: Oct. 6, 2014,   recorded: December 2013,   views: 3188
Categories

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

Transfer learning has attracted increasingly attention in artificial intelligence, machine learning and many other application areas. Different from traditional machine learning methods which assume the training and testing data come from the same task or domain, transfer learning aims to extract common knowledge across domains or tasks, such that a model trained on one domain or task can be adapted to other domains or tasks. In this talk, I will first give an overview of transfer learning and discuss the relationships between transfer learning and other learning areas, and then summarize various transfer learning approaches into several categories, and introduce some representative methods, finally discuss research challenges and future directions in this area.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: