  Multi-task learning (MTL) is an important subject in machine learning and
artificial intelligence. Its applications to computer vision, signal
processing, and speech recognition are ubiquitous. Although this subject has
attracted considerable attention recently, the performance and robustness of
the existing models to different tasks have not been well balanced. This
article proposes an MTL model based on the architecture of the variational
information bottleneck (VIB), which can provide a more effective latent
representation of the input features for the downstream tasks. Extensive
observations on three public data sets under adversarial attacks show that the
proposed model is competitive to the state-of-the-art algorithms concerning the
prediction accuracy. Experimental results suggest that combining the VIB and
the task-dependent uncertainties is a very effective way to abstract valid
information from the input features for accomplishing multiple tasks.
