TY  - BOOK
AU  - Mohammad J.Hashemi
TI  - General Domain Adaptation Through Proportional Progressive Pseudo Labeling
PY  - 2020///
PB  - arXiv
N1  - Open access — freely available to read
N2  - Domain 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
UR  - https://arxiv.org/pdf/2012.13028v1
ER  -