Deep Least Squares Alignment for Unsupervised Domain Adaptation

Youshan Zhang and Brian D. Davison

Full Paper (14 pages)
Author's version: PDF
Supplementary Material

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.

In Proceedings of the 32nd British Machine Vision Conference (BMVC), November 2021.

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Last modified: 26 November 2021
Brian D. Davison