  Source free domain adaptation (SFDA) aims to transfer a trained source model
to the unlabeled target domain without accessing the source data. However, the
SFDA setting faces an effect bottleneck due to the absence of source data and
target supervised information, as evidenced by the limited performance gains of
newest SFDA methods. In this paper, for the first time, we introduce a more
practical scenario called active source free domain adaptation (ASFDA) that
permits actively selecting a few target data to be labeled by experts. To
achieve that, we first find that those satisfying the properties of
neighbor-chaotic, individual-different, and target-like are the best points to
select, and we define them as the minimum happy (MH) points. We then propose
minimum happy points learning (MHPL) to actively explore and exploit MH points.
We design three unique strategies: neighbor ambient uncertainty, neighbor
diversity relaxation, and one-shot querying, to explore the MH points. Further,
to fully exploit MH points in the learning process, we design a neighbor focal
loss that assigns the weighted neighbor purity to the cross-entropy loss of MH
points to make the model focus more on them. Extensive experiments verify that
MHPL remarkably exceeds the various types of baselines and achieves significant
performance gains at a small cost of labeling.
