TY - GEN
T1 - Towards Analyzing the Robustness of Deep Light-weight Image Super Resolution Networks under Distribution Shift
AU - Esmaeilzehi, Alireza
AU - Ma, Lei
AU - Ahmad, M. Omair
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Deep light-weight image super resolution networks that provide a high performance have numerous real-life applications, such as mobile devices and multimedia systems. Hence, analyzing the capability of such deep networks in providing a similar performance between the cases that they are applied to the images with and without distributions similar to that of the training is crucial. In this paper, we carry out the robustness analysis of the deep state-of-the-art light-weight super resolution networks by proposing and using three metrics that are based on the statistical information of the super resolved images in both pixel level and feature level. The results of our metrics for the deep state-of-the-art light-weight super resolution networks demonstrate the behavior of such networks against realistic distribution shift in the test dataset.
AB - Deep light-weight image super resolution networks that provide a high performance have numerous real-life applications, such as mobile devices and multimedia systems. Hence, analyzing the capability of such deep networks in providing a similar performance between the cases that they are applied to the images with and without distributions similar to that of the training is crucial. In this paper, we carry out the robustness analysis of the deep state-of-the-art light-weight super resolution networks by proposing and using three metrics that are based on the statistical information of the super resolved images in both pixel level and feature level. The results of our metrics for the deep state-of-the-art light-weight super resolution networks demonstrate the behavior of such networks against realistic distribution shift in the test dataset.
UR - http://www.scopus.com/inward/record.url?scp=85143610839&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143610839&partnerID=8YFLogxK
U2 - 10.1109/MMSP55362.2022.9948963
DO - 10.1109/MMSP55362.2022.9948963
M3 - Conference contribution
AN - SCOPUS:85143610839
T3 - 2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022
BT - 2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Workshop on Multimedia Signal Processing, MMSP 2022
Y2 - 26 September 2022 through 28 September 2022
ER -