Learning kernels for visual domain adaptation

author: Fei Sha, Computer Science Department, University of Southern California
published: Oct. 6, 2014,   recorded: December 2013,   views: 2005

Related Open Educational Resources

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.


Statistical machine learning has become an important driving force behind many application fields. By large, however, its theoretical underpinning has hinged on the stringent assumption that the learning environment is stationary. In particular, the data distribution on which statistical models are optimized is the same as the distribution to which the models are applied.

Real-world applications are far more complex than the pristine condition. For instance, computer vision systems for recognizing objects in images often suffer from significant performance degradation if they are evaluated on image datasets that are different from the dataset on which they are designed.

In this talk, I will describe our efforts in addressing this important challenge of building intelligent systems that are robust to distribution disparity. The central theme is to learn invariant features, cast as learning kernel functions and adapt probabilistic models across different distributions (i.e., domains). To this end,our key insight is to discover and exploit hidden structures in the data. These structures, such as manifolds and discriminative clusters,are intrinsic and thus resilient to distribution changes due to exogenous factors. I will present several learning algorithms we have proposed and demonstrate their effectiveness in pattern recognition tasks from computer vision.

This talk is based on the joint work with my students (Boqing Gong and Yuan Shi, both from USC) and our collaborator Prof. Kristen Grauman (U. of Texas, Austin).

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: