LRS-DAG: Low Resource Supervised Domain Adaptation with Generalization Across Domains
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Current state of the art methods in Domain Adaptation follow adversarial approaches, making training a challenge. Other non-adversarial methods learn mappings between source and target domains, to achieve reasonable performance. However, even these methods do not focus a key aspect of maintaining performance on the source domain, even after optimizing over the target domain. Additionally, there exist very few methods in low resource supervised domain adaptation. This work proposes a method, LRS-DAG, that aims to solve these current issues in the field. By adding a set of "encoder layers" which map the target domain to the source, and can be removed when dealing directly with the source data, the model learns to perform optimally on both domains. LRS-DAG is unique in the sense that a new algorithm for low resource domain adaptation, which maintains performance over the source, with a new metric for learning mappings has been introduced.
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