000			01414nam a2200169 a 4500
005			20260901030329.0
008			250101s2020 xx o 000 0 eng d
100	1		_aMohammad J. Hashemi
245	1	0	_aGeneral Domain Adaptation Through Proportional Progressive Pseudo Labeling
264		1	_barXiv _c2020
336			_atext
338			_aonline resource
520			_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 train
506	0		_aOpen access — freely available to read.
856	4	0	_uhttps://arxiv.org/pdf/2012.13028v1 _yRead the full paper (PDF)
942			_cERES
999			_c643 _d643