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Title: Continous Representation Learning via User Feedback

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:
OSTI Identifier:
Report Number(s):
Continous Representation Learning via User Feedbac; 004877MLTPL00
Battelle IPID 30871-E
DOE Contract Number:
Resource Type:
Software Revision:
Software Package Number:
Software CPU:
Source Code Available:
Other Software Info:
Copyright software available through PNNL Technology Commercialization.
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
Contributing Orgs:
Battelle Memorial Institute, Pacific Northwest Division (PNNL)
Country of Publication:
United States

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