Improving deep convultional neural networks with unsupervised feature learning
Nguyen Thanh, Kien, Fookes, Clinton B., & Sridharan, Sridha (2015) Improving deep convultional neural networks with unsupervised feature learning. In Proceedings - International Conference on Image Processing 2015, IEEE, Quebec City, Canada.
The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.
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|Item Type:||Conference Paper|
|Keywords:||deep learning, convolutional neural network, unsupervised feature learning|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||© 2015 IEEE|
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|Deposited On:||11 Sep 2015 03:14|
|Last Modified:||12 Sep 2015 20:07|
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