ORCID Identifiers
Document Type
Article
Source of Publication
IEEE Access
Publication Date
1-1-2021
Abstract
The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do not scale to small regional hospitals. One of the clinical challenges addressed in this work is magnetic resonance image generalization for improved visualization and diagnosis of hip abnormalities such as femoroacetabular impingement and dysplasia. Domain Generalization (DG) is a field in machine learning that tries to solve the model’s dependency on the training data by leveraging many related but different data sources. We present a new method for DG that is both efficient and fast, unlike the most current state of art methods, which add a substantial computational burden making it hard to fine-tune. Our model trains an autoencoder setting on top of the classifier, but the encoder is trained on the adversarial reconstruction loss forcing it to forget style information while extracting features useful for classification. Our approach aims to force the encoder to generate domain-invariant representations that are still category informative by pushing it in both directions. Our method has proven universal and was validated on four different benchmarks for domain generalization, outperforming state of the art on RMNIST, VLCS and IXMAS with a 0.70% increase in accuracy and providing comparable results on PACS with a 0.02% difference. Our method was also evaluated for unsupervised domain adaptation and has shown to be quite an effective method against over-fitting.
DOI Link
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Disciplines
Computer Sciences
Keywords
Computational modeling, Computer Vision, Data models, Deep Learning, Deep learning, Domain Adaptation, Domain Generalization, Feature extraction, Image reconstruction, Training, Transfer Learning, Transfer learning
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Ibrahim, Bekkouch Imad Eddine; Nicolae, Dragos Constantin; Khan, Adil; Ahsan Kazmi, S. M.; Khattak, Asad Masood; and Ibragimov, Bulat, "Adversarial Reconstruction Loss for Domain Generalization" (2021). All Works. 4121.
https://zuscholars.zu.ac.ae/works/4121
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series