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Title: Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification
Convolutional Neural Network (CNN) models and many accessible large-scale public visual datasets have brought lots of research work to a remarkable new stage. Benefited from well-trained CNN models, small training datasets can learn comprehensive features by utilizing the preliminary features from transfer learning. However, the performance is not guaranteed when taking these features to construct a new model, as the differences always exist between the source and target domains. In this paper, we propose to build an Evolution Programming-based framework to address various challenges. This framework automates both the feature learning and model building processes. It first identifies the most valuable features from pre-trained models and then constructs a suitable model to understand the characteristic features for different tasks. Each model differs in numerous ways. Overall, the experimental results effectively reach optimal solutions, demonstrating that a time-consuming task could also be conducted by an automated process that exceeds the human ability.  more » « less
Award ID(s):
1952089
NSF-PAR ID:
10233983
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Information systems frontiers
Volume:
22
ISSN:
1572-9419
Page Range / eLocation ID:
1053-1066
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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