1 / 16

Data Manipulation and Creation Techniques for Learning Tasks

Data Manipulation and Creation Techniques for Learning Tasks. Ashutosh Saxena In collaboration with: Ben Sapp, Justin Driemeyer, Jeff Michels, Prof. Andrew Y. Ng. Stanford University. Data. The issue of what data is there to learn from is at the heart of many machine learning algorithms.

valencia
Download Presentation

Data Manipulation and Creation Techniques for Learning Tasks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Manipulation and Creation Techniques for Learning Tasks Ashutosh Saxena In collaboration with: Ben Sapp, Justin Driemeyer, Jeff Michels, Prof. Andrew Y. Ng Stanford University

  2. Data • The issue of what data is there to learn from is at the heart of many machine learning algorithms. • Often, an inferior learning algorithm outperforms a superior one, if it is given more data to learn from. • Methods to increase dataset sizes have same potential as better learning algorithms. Creating data Labeled Learning Data Learning algorithms Goal

  3. Data • The issue of what data is there to learn from is at the heart of many machine learning algorithms. • Two synthetic dataset creation techniques. • Successfully applied to three learning problems in vision/robotics domain. Creating data Labeled Learning Data Learning algorithms Goal

  4. Data Manipulation Techniques Task: Object Classification • Create synthetic data using green screen. Green screen + + = Manipulated Objects Shape Background Foreground

  5. Object Classification Results Mugs on Caltech-256 dataset. Train on ~1e5 images. Curve does not asymptote. Number of training examples 

  6. Synthetic Data Creation Task: Robotic Grasping • Create object images using computer graphics. • Train on ~1e4 images (ISER 2006, NIPS 2006, IJRR 2007.)

  7. Results: Novel Objects (ISER 2006, NIPS 2006, IJRR 2007)

  8. Results: Novel Objects (3-x) (ISER 2006, NIPS 2006, IJRR 2007)

  9. Results: Dishwasher (ISER 2006, NIPS 2006, IJRR 2007)

  10. Results: Dishwasher (2-x) (ISER 2006, NIPS 2006, IJRR 2007)

  11. Quantitative experiments • Uncluttered environment • Dishwasher

  12. Synthetic Data Creation Task: Autonomous monocular driving • Create image+depths using computer graphics. • Train on ~1e4 images+depths Images/depths of synthetic environments (ICML 2005.)

  13. (ICML 2005.)

  14. Thank you http://ai.stanford.edu/~asaxena

More Related