TY - GEN
T1 - Can the early human visual system compete with deep neural networks?
AU - Dodge, Samuel
AU - Karam, Lina
PY - 2017/7/1
Y1 - 2017/7/1
N2 - We study and compare the human visual system and state-of-The-Art deep neural networks on classification of distorted images. Different from previous works, we limit the display time to 100ms to test only the early mechanisms of the human visual system, without allowing time for any eye movements or other higher level processes. Our findings show that the human visual system still outperforms modern deep neural networks under blurry and noisy images. These findings motivate future research into developing more robust deep networks.
AB - We study and compare the human visual system and state-of-The-Art deep neural networks on classification of distorted images. Different from previous works, we limit the display time to 100ms to test only the early mechanisms of the human visual system, without allowing time for any eye movements or other higher level processes. Our findings show that the human visual system still outperforms modern deep neural networks under blurry and noisy images. These findings motivate future research into developing more robust deep networks.
UR - http://www.scopus.com/inward/record.url?scp=85046287206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046287206&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2017.329
DO - 10.1109/ICCVW.2017.329
M3 - Conference contribution
AN - SCOPUS:85046287206
T3 - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
SP - 2798
EP - 2804
BT - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Y2 - 22 October 2017 through 29 October 2017
ER -