01569nam a2200181 a 450000500170000000800400001710000240005724500790008126400160016033600090017633800200018552009050020550600460111085600660115694200090122299900130123195201430124420260901030329.0250101s2020    xx     o     000 0 eng d1 aMohammad J. Hashemi10aGeneral Domain Adaptation Through Proportional Progressive Pseudo Labeling 1barXivc2020  atext  aonline resource  aDomain adaptation helps transfer the knowledge gained from a labeled source domain to an unlabeled target domain. During the past few years, different domain adaptation techniques have been published. One common flaw of these approaches is that while they might work well on one input type, such as images, their performance drops when applied to others, such as text or time-series. In this paper, we introduce Proportional Progressive Pseudo Labeling (PPPL), a simple, yet effective technique that can be implemented in a few lines of code to build a more general domain adaptation technique that can be applied on several different input types. At the beginning of the training phase, PPPL progressively reduces target domain classification error, by training the model directly with pseudo-labeled target domain samples, while excluding samples with more likely wrong pseudo-labels from the train0 aOpen access — freely available to read.40uhttps://arxiv.org/pdf/2012.13028v1yRead the full paper (PDF)  cERES  c643d643  001040738CYBaMAINbMAINcSCICOMPd2026-09-01l0pYGE000904r2026-09-01 03:03:30uhttps://arxiv.org/pdf/2012.13028v1w2026-09-01yPAPER