  One of the ways to improve the performance of a target task is to learn the
transfer of abundant knowledge of a pre-trained network. However, learning of
the pre-trained network requires high computation capability and large-scale
labeled dataset. To mitigate the burden of large-scale labeling, learning in
un/self-supervised manner can be a solution. In addition, using unsupervised
multi-task learning, a generalized feature representation can be learned.
However, unsupervised multi-task learning can be biased to a specific task. To
overcome this problem, we propose the metric-based regularization term and
temporal task ensemble (TTE) for multi-task learning. Since these two
techniques prevent the entire network from learning in a state deviated to a
specific task, it is possible to learn a generalized feature representation
that appropriately reflects the characteristics of each task without biasing.
Experimental results for three target tasks such as classification, object
detection and embedding clustering prove that the TTE-based multi-task
framework is more effective than the state-of-the-art (SOTA) method in
improving the performance of a target task.
