Sample Selection for Universal Domain Adaptation
Omri Lifshitz, Lior Wolf
Abstract
This paper studies the problem of unsupervised domain adaption in the universal scenario, in which only some of the classes are shared between the source and target domains. We present a scoring scheme that is effective in identifying the samples of the shared classes. The score is used to select samples in the target domain for which to apply specific losses during training; pseudo-labels for high scoring samples and confidence regularization for low scoring samples. Taken together, our method is shown to outperform, by a sizeable margin, the current state of the art on the literature benchmarks.
Topics & Concepts
Regularization (linguistics)Domain adaptationMargin (machine learning)Computer scienceDomain (mathematical analysis)Artificial intelligenceSelection (genetic algorithm)Machine learningAdaptation (eye)Sample (material)Scheme (mathematics)Pattern recognition (psychology)MathematicsPsychologyClassifier (UML)ChromatographyNeuroscienceChemistryMathematical analysisDomain Adaptation and Few-Shot LearningMultimodal Machine Learning ApplicationsMachine Learning and ELM