DynaShare: Task and Instance Conditioned Parameter Sharing for
  Multi-Task Learning
Elahe Rahimian Golara Javadi Frederick Tung Gabriel Oliveira
Multi-task networks rely on effective parameter sharing to achieve robust
generalization across tasks. In this paper, we present a novel parameter
sharing method for multi-task learning that conditions parameter sharing on
both the task and the intermediate feature representations at inference time.
In contrast to traditional parameter sharing approaches, which fix or learn a
deterministic sharing pattern during training and apply the same pattern to all
examples during inference, we propose to dynamically decide which parts of the
network to activate based on both the task and the input instance. Our approach
learns a hierarchical gating policy consisting of a task-specific policy for
coarse layer selection and gating units for individual input instances, which
work together to determine the execution path at inference time. Experiments on
the NYU v2, Cityscapes and MIMIC-III datasets demonstrate the potential of the
proposed approach and its applicability across problem domains.