A Principled Approach for Learning Task Similarity in Multitask Learning

Understand

Multitask learning aims at solving a set of related tasks simultaneously, by exploiting the shared knowledge for improving the performance on individual tasks.

• Hence, an important aspect of multitask learning is to understand the similarities within a set of tasks.
• Previous works have incorporated this similarity information explicitly (e.g., weighted loss for each task) or implicitly (e.g., adversarial loss for feature adaptation), for achieving good empirical performances.
• However, the theoretical motivations for adding task similarity knowledge are often missing or incomplete.