Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2018
Abstract
In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions, limited labeled data samples, etc. In this work, we are making efforts to deal with these challenges through a computational framework by incorporating latest developments in deep learning. In terms of two-stage detection scheme, pseudo labeling, data augmentation, cross-validation and ensemble learning, the proposed framework aims to achieve better performances for practical image recognition applications as compared to using standard deep learning methods. The proposed framework has recently been deployed as the key kernel for several image recognition competitions organized by Kaggle. The performance is promising as our final private scores were ranked 4 out of 2293 teams for fish recognition on the challenge “The Nature Conservancy Fisheries Monitoring” and 3 out of 834 teams for cervix recognition on the challenge “Intel & MobileODT Cervical Cancer Screening”, and several others. We believe that by sharing the solutions, we can further promote the applications of deep learning techniques.
Keywords
And image classification, Image recognition, Deep learning, Objection detection
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, August 19-23
First Page
923
Last Page
931
ISBN
9781450355520
Identifier
10.1145/3219819.3219907
Publisher
ACM
City or Country
New York
Citation
YANG, Xulei; ZENG, Zeng; TEO, Sin G.; WANG, Li; CHANDRASEKAR, Vijay; and HOI, Steven C. H..
Deep learning for practical image recognition: Case study on kaggle competitions. (2018). KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, August 19-23. 923-931.
Available at: https://ink.library.smu.edu.sg/sis_research/4184
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3219819.3219907
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons