Hard parameter sharing in multi-task learning (MTL) allows tasks to share some of model parameters, reducing storage cost and improving prediction accuracy. The common sharing practice is to share bottom layers of a deep neural network among tasks while using separate top layers for each task. In this work, we revisit this common practice via an empirical study on fine-grained image classification tasks and make two surprising observations. (1) Using separate bottom-layer parameters could achieve significantly better performance than the common practice and this phenomenon holds for different number of tasks jointly trained on different backbone architectures with different quantity of task-specific parameters. (2) A multi-task model with a small proportion of task-specific parameters from bottom layers can achieve competitive performance with independent models trained on each task separately and outperform a state-of-the-art MTL framework. Our observations suggest that people rethink the current sharing paradigm and adopt the new strategy of using separate bottom-layer parameters as a stronger baseline for model design in MTL.
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