TY - JOUR

T1 - Adversarial Reconstruction Loss for Domain Generalization

AU - Bekkouch, Imad Eddine Ibrahim

AU - Nicolae, Dragos Constantin

AU - Khan, Adil

AU - Kazmi, S. M.Ahsan

AU - Khattak, Asad Masood

AU - Ibragimov, Bulat

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Computer vision

KW - deep learning

KW - domain adaptation

KW - domain generalization

KW - transfer learning

UR - http://www.scopus.com/inward/record.url?scp=85103388309&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2021.3066041

DO - 10.1109/ACCESS.2021.3066041

M3 - Journal article

AN - SCOPUS:85103388309

VL - 9

SP - 42424

EP - 42437

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 9378518

ER -