Deep Least Squares Alignment for Unsupervised Domain Adaptation
- URL: http://arxiv.org/abs/2111.02207v1
- Date: Wed, 3 Nov 2021 13:23:06 GMT
- Title: Deep Least Squares Alignment for Unsupervised Domain Adaptation
- Authors: Youshan Zhang and Brian D. Davison
- Abstract summary: Unsupervised domain adaptation leverages rich information from a labeled source domain to model an unlabeled target domain.
We propose deep least squares alignment (DLSA) to estimate the distribution of the two domains in a latent space by parameterizing a linear model.
Extensive experiments demonstrate that the proposed DLSA model is effective in aligning domain distributions and outperforms state-of-the-art methods.
- Score: 6.942003070153651
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Unsupervised domain adaptation leverages rich information from a labeled
source domain to model an unlabeled target domain. Existing methods attempt to
align the cross-domain distributions. However, the statistical representations
of the alignment of the two domains are not well addressed. In this paper, we
propose deep least squares alignment (DLSA) to estimate the distribution of the
two domains in a latent space by parameterizing a linear model. We further
develop marginal and conditional adaptation loss to reduce the domain
discrepancy by minimizing the angle between fitting lines and intercept
differences and further learning domain invariant features. Extensive
experiments demonstrate that the proposed DLSA model is effective in aligning
domain distributions and outperforms state-of-the-art methods.
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