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Title: Continous Representation Learning via User Feedback

Abstract

Representation learning is a deep-learning based technique for extracting features from data for the purpose of machine learning. This requires a large amount of data, on order tens of thousands to millions of samples, to properly teach the deep neural network. This a system for continuous representation learning, where the system may be improved with a small number of additional samples (order 10-100). The unique characteristics of this invention include a human-computer feedback component, where assess the quality of the current representation and then provides a better representation to the system. The system then mixes the new data with old training examples to avoid overfitting and improve overall performance of the system. The model can be exported and shared with other users, and it may be applied to additional images the system hasn't seen before.

Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
Battelle Memorial Institute, Pacific Northwest Division (PNNL)
OSTI Identifier:
1307215
Report Number(s):
Continous Representation Learning via User Feedbac; 004877MLTPL00
Battelle IPID 30871-E
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Software
Software Revision:
00
Software Package Number:
004877
Software CPU:
MLTPL
Source Code Available:
No
Other Software Info:
Copyright software available through PNNL Technology Commercialization.
Country of Publication:
United States

Citation Formats

. Continous Representation Learning via User Feedback. Computer software. Vers. 00. USDOE. 8 Aug. 2016. Web.
. (2016, August 8). Continous Representation Learning via User Feedback (Version 00) [Computer software].
. Continous Representation Learning via User Feedback. Computer software. Version 00. August 8, 2016.
@misc{osti_1307215,
title = {Continous Representation Learning via User Feedback, Version 00},
author = {},
abstractNote = {Representation learning is a deep-learning based technique for extracting features from data for the purpose of machine learning. This requires a large amount of data, on order tens of thousands to millions of samples, to properly teach the deep neural network. This a system for continuous representation learning, where the system may be improved with a small number of additional samples (order 10-100). The unique characteristics of this invention include a human-computer feedback component, where assess the quality of the current representation and then provides a better representation to the system. The system then mixes the new data with old training examples to avoid overfitting and improve overall performance of the system. The model can be exported and shared with other users, and it may be applied to additional images the system hasn't seen before.},
doi = {},
year = {Mon Aug 08 00:00:00 EDT 2016},
month = {Mon Aug 08 00:00:00 EDT 2016},
note =
}

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