Network Clustering for Multi-task Learning

Understand

The Multi-Task Learning (MTL) technique has been widely studied by word-wide researchers.

• The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task.
• Since the specific layers directly follow the hard layers, the MTL model needs to estimate this direct change (from general to specific) as well.
• To alleviate this problem, we introduce the novel cluster layer, which groups tasks into clusters during training procedures.