Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

Filling the GAPs: Reducing the Complexity of Networks for Multi-attribute Image Aesthetic Prediction

Abstract: 

Computational aesthetics have seen much progress in recent years with the increasing popularity of deep learning methods. In this paper, we present two approaches that leverage on the benefits of using Global Average Pooling (GAP) to reduce the complexity of deep convolutional neural networks. The first model fine-tunes a standard CNN with a newly introduced GAP layer. The second approach extracts global and local CNN codes by reducing the dimensionality of convolution layers with individual GAP operations. We also extend these approaches to a multi-attribute network which uses a style network to regularize the aesthetic network. Experiments demonstrate the capability of attaining comparable accuracy results while reducing training complexity substantially.

up
0 users have voted:

Paper Details

Authors:
Magzhan Kairanbay
Magzhan Kairanbay, John See, Lai-Kuan Wong, Yong-Lian Hii
Submitted On:
14 September 2017 - 11:17pm
Short Link:
Type:
Poster
Event:
ICIP 2017
Presenter's Name:
Magzhan Kairanbay
Paper Code:
3435
Document Year:
2017
Cite

Document Files

poster.pdf

(894)

Keywords

Subscribe

Send Author a Private Message
[1] Magzhan Kairanbay, John See, Lai-Kuan Wong, Yong-Lian Hii, "Filling the GAPs: Reducing the Complexity of Networks for Multi-attribute Image Aesthetic Prediction", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2075. Accessed: Jun. 19, 2019.
@article{2075-17,
url = {http://sigport.org/2075},
author = {Magzhan Kairanbay; John See; Lai-Kuan Wong; Yong-Lian Hii },
publisher = {IEEE SigPort},
title = {Filling the GAPs: Reducing the Complexity of Networks for Multi-attribute Image Aesthetic Prediction},
year = {2017} }
TY - EJOUR
T1 - Filling the GAPs: Reducing the Complexity of Networks for Multi-attribute Image Aesthetic Prediction
AU - Magzhan Kairanbay; John See; Lai-Kuan Wong; Yong-Lian Hii
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2075
ER -
Magzhan Kairanbay, John See, Lai-Kuan Wong, Yong-Lian Hii. (2017). Filling the GAPs: Reducing the Complexity of Networks for Multi-attribute Image Aesthetic Prediction. IEEE SigPort. http://sigport.org/2075
Magzhan Kairanbay, John See, Lai-Kuan Wong, Yong-Lian Hii, 2017. Filling the GAPs: Reducing the Complexity of Networks for Multi-attribute Image Aesthetic Prediction. Available at: http://sigport.org/2075.
Magzhan Kairanbay, John See, Lai-Kuan Wong, Yong-Lian Hii. (2017). "Filling the GAPs: Reducing the Complexity of Networks for Multi-attribute Image Aesthetic Prediction." Web.
1. Magzhan Kairanbay, John See, Lai-Kuan Wong, Yong-Lian Hii. Filling the GAPs: Reducing the Complexity of Networks for Multi-attribute Image Aesthetic Prediction [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2075