Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study

Understand

Multi-Task Learning has emerged as a methodology in which multiple tasks are jointly learned by a shared learning algorithm, such as a DNN.

• MTL is based on the assumption that the tasks under consideration are related; therefore it exploits shared knowledge for improving performance on each individual task.
• Tasks are generally considered to be homogeneous, i.e., to refer to the same type of problem.
• Moreover, MTL is usually based on ground truth annotations with full, or partial overlap across tasks.