Table 1 Domain adaptation model based on pseudo-labelling

\(X_{s}\): Dataset in the source domain

\(X_{t}\): Dataset in the target domain

\(X_{l}\): Labelled training data set initialized as the source data \(X_{s}\)

\(G\): Classifier trained on \(T\)

\(Q\): Selective pseudo-labelling strategy

Initialization

Train the classifier \(G\) on the labelled training data set \(X_{l}\)

Assign pseudo-labels for all the data in \(X_{t}\) using the classifier \(G\)

Repeat:

Select pseudo-labelled samples in \(X_{t}\) according to \(Q\)

Add samples selected in (3) to \(X_{l}\)

Retrain the classifier \(G\)

Until the iteration condition is met or the pseudo-labels no longer change