On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem.

Bibliographic Details
Title: On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem.
Authors: Honglan Huang, Jincai Huang, Yanghe Feng, Jiarui Zhang, Zhong Liu, Qi Wang, Li Chen
Source: PLoS ONE, Vol 14, Iss 6, p e0217408 (2019)
Publisher Information: Public Library of Science (PLoS), 2019.
Publication Year: 2019
Collection: LCC:Medicine
LCC:Science
Subject Terms: Medicine, Science
More Details: As a promising research direction in recent decades, active learning allows an oracle to assign labels to typical examples for performance improvement in learning systems. Existing works mainly focus on designing criteria for screening examples of high value to be labeled in a handcrafted manner. Instead of manually developing strategies of querying the user to access labels for the desired examples, we utilized the reinforcement learning algorithm parameterized with the neural network to automatically explore query strategies in active learning when addressing stream-based one-shot classification problems. With the involvement of cross-entropy in the loss function of Q-learning, an efficient policy to decide when and where to predict or query an instance is learned through the developed framework. Compared with a former influential work, the advantages of our method are demonstrated experimentally with two image classification tasks, and it exhibited better performance, quick convergence, relatively good stability and fewer requests for labels.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1932-6203
Relation: https://doaj.org/toc/1932-6203
DOI: 10.1371/journal.pone.0217408
Access URL: https://doaj.org/article/38c11e8c6f284674b3e4c329e9955dee
Accession Number: edsdoj.38c11e8c6f284674b3e4c329e9955dee
Database: Directory of Open Access Journals
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More Details
ISSN:19326203
DOI:10.1371/journal.pone.0217408
Published in:PLoS ONE
Language:English