In this paper we propose a semi-supervised multiple instance learning based boosting algorithm for domain adaptation, with face detection as an example. We pool the putative positives on a given test image into a positive bag and the putative negatives into a negative bag. We augment this data to the initial training data and retrain the classifier using MILBoost. We show that our approach outperforms self-learning and compares favorably with MILBoost trained on manually marked face data without the corresponding increase in labeling effort.