Algorithm 1 General Active Learning

Input: initial train set L, unlabelled validation set U, independent test set T

Output: predicted labels for the test set

1:$\theta \leftarrow clf.fit(L)$▹ Learns model on initial training set 2:while SC not met do 3:    selection by $QS$ of the most informative sample: $x^{*}$ 4:    ask Oracle for $x^{*}$ label 5:    $L\leftarrow L\cup x^{*}$▹ Increments the model’s training set with $x^{*}$ 6:    $U\leftarrow U\setminus x^{*}$▹ Removes $x^{*}$ from unlabelled samples U 7:    $\Theta \leftarrow clf.fit(L)$▹ Updates model 8:    return $clf.predict(T)$▹ Returns predicted labels for the test set 9: end while