  Deep learning has made revolutionary advances to diverse applications in the
presence of large-scale labeled datasets. However, it is prohibitively
time-costly and labor-expensive to collect sufficient labeled data in most
realistic scenarios. To mitigate the requirement for labeled data,
semi-supervised learning (SSL) focuses on simultaneously exploring both labeled
and unlabeled data, while transfer learning (TL) popularizes a favorable
practice of fine-tuning a pre-trained model to the target data. A dilemma is
thus encountered: Without a decent pre-trained model to provide an implicit
regularization, SSL through self-training from scratch will be easily misled by
inaccurate pseudo-labels, especially in large-sized label space; Without
exploring the intrinsic structure of unlabeled data, TL through fine-tuning
from limited labeled data is at risk of under-transfer caused by model shift.
To escape from this dilemma, we present Self-Tuning, a novel approach to enable
data-efficient deep learning by unifying the exploration of labeled and
unlabeled data and the transfer of a pre-trained model. Further, to address the
challenge of confirmation bias in self-training, a Pseudo Group Contrast (PGC)
mechanism is devised to mitigate the reliance on pseudo-labels and boost the
tolerance to false-labels. Self-Tuning outperforms its SSL and TL counterparts
on five tasks by sharp margins, e.g. it doubles the accuracy of fine-tuning on
Cars with 15% labels.
