Image quality assessment based label smoothing in deep neural network learning
For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i.e., artifact-free). However, digital images are subject to a wide range of distortions in real application scenarios, while the practical issues regardi...
Saved in:
Main Author: | Chen, Zhou |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Research Report |
Language: | English |
Published: |
2018
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/73386 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Image recognition based on deep learning of convolutional neural networks
by: Xie, Cong
Published: (2019) -
A deep learning approach to image quality assessment
by: Feng, Yeli
Published: (2019) -
Geometry estimation by deep neural network
by: Mei, Jianhan
Published: (2022) -
Deep learning for medical image analysis
by: Yang, Ivan Sze Yuan
Published: (2020) -
Automated image quality assessment and its applications in computer vision
by: Zhou, Phoebe Huixin
Published: (2022)