  Multi-task Learning (MTL) for classification with disjoint datasets aims to
explore MTL when one task only has one labeled dataset. In existing methods,
for each task, the unlabeled datasets are not fully exploited to facilitate
this task. Inspired by semi-supervised learning, we use unlabeled datasets with
pseudo labels to facilitate each task. However, there are two major issues: 1)
the pseudo labels are very noisy; 2) the unlabeled datasets and the labeled
dataset for each task has considerable data distribution mismatch. To address
these issues, we propose our MTL with Selective Augmentation (MTL-SA) method to
select the training samples in unlabeled datasets with confident pseudo labels
and close data distribution to the labeled dataset. Then, we use the selected
training samples to add information and use the remaining training samples to
preserve information. Extensive experiments on face-centric and human-centric
applications demonstrate the effectiveness of our MTL-SA method.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning