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What is: ALDA?

SourceAdversarial-Learned Loss for Domain Adaptation
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

Adversarial-Learned Loss for Domain Adaptation is a method for domain adaptation that combines adversarial learning with self-training. Specifically, the domain discriminator has to produce different corrected labels for different domains, while the feature generator aims to confuse the domain discriminator. The adversarial process finally leads to a proper confusion matrix on the target domain. In this way, ALDA takes the strengths of domain-adversarial learning and self-training based methods.