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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Unsupervised Domain Adaptation using Maximum Mean Covariance Discrepancy and Variational Autoencoder

Author 1: Fabian Barreto
Author 2: Jignesh Sarvaiya
Author 3: Suprava Patnaik
Author 4: Sushilkumar Yadav

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.01306104

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 6, 2022.

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Abstract: Face Recognition has progressed tremendously from its initial use of holistic learning models to using hand-crafted, shallow, and deep learning models. DeepFace, a nine-layer Deep Convolutional Neural Network (DCNN), reached near-human performance on unconstrained face recognition for the La-beled Faces in the Wild (LFW) dataset. These models performed very well on the benchmark datasets, but their performance sometimes deteriorated for real-world applications. The problem arose when there was a domain shift due to different distribution spaces of the training and testing models. Few researchers looked at Unsupervised Domain Adaptation (UDA) to find the domain-invariant feature spaces. They tried to minimize the domain discrepancy using a static loss of maximum mean discrepancy (MMD). From MMD, the researchers delved into the higher-order statistics of maximum covariance discrepancy (MCD). MMD and MCD were combined to get maximum mean and covariance discrepancy (MMCD), which captured more information than MMD alone. We use a Variational Autoencoder (VAE) with joint mean and covariance discrepancy to offer a solution for domain adaptation. The proposed MMCD-VAE model uses VAE to measure the discrepancy in the spread of variance around the mean value and uses MMCD to measure the directional discrepancy in the variance. Analysis was done using the TinyFace benchmark dataset and the Bollywood Celebrities dataset. Three objective image quality parameters, namely SSIM, pieAPP, and SIFT feature matching, demonstrate the superiority of MMCD-VAE over the conventional KL-VAE model. MMCD-VAE shows an 18 % improvement in SSIM and a remarkable improvement in the perceptual quality of the image over the conventional KL-VAE model.

Keywords: Deep learning; domain adaptation; face recognition; maximum mean covariance discrepancy; transfer learning; varia-tional autoencoders

Fabian Barreto, Jignesh Sarvaiya, Suprava Patnaik and Sushilkumar Yadav, “Unsupervised Domain Adaptation using Maximum Mean Covariance Discrepancy and Variational Autoencoder” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01306104

@article{Barreto2022,
title = {Unsupervised Domain Adaptation using Maximum Mean Covariance Discrepancy and Variational Autoencoder},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01306104},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01306104},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Fabian Barreto and Jignesh Sarvaiya and Suprava Patnaik and Sushilkumar Yadav}
}


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