Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2006.04996v1
• Date: Tue, 9 Jun 2020 00:20:21 GMT
• Title: Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
• Authors: Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei
• Abstract summary: Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain. We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly. We present a sampling-based implicit alignment approach, where the sample selection procedure is implicitly guided by the pseudo-labels.
• Score: 18.90240379173491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain. However, these methods suffer from pseudo-label bias in the form of error accumulation. We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly. Instead, we present a sampling-based implicit alignment approach, where the sample selection procedure is implicitly guided by the pseudo-labels. Theoretical analysis reveals the existence of a domain-discriminator shortcut in misaligned classes, which is addressed by the proposed implicit alignment approach to facilitate domain-adversarial learning. Empirical results and ablation studies confirm the effectiveness of the proposed approach, especially in the presence of within-domain class imbalance and between-domain class distribution shift.

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