  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 training set
and also postponing training on such samples. Experiments on 6 different
datasets that include tasks such as anomaly detection, text sentiment analysis
and image classification demonstrate that PPPL can beat other baselines and
generalize better.
