Abstract Multi-task neural architecture search (MTNAS), which searches for a shared architecture for multiple tasks, has been broadly investigated. In these methods, multiple tasks are learned simultaneously by minimizing the weighted sum of their losses. How to balance these losses by finding the optimal loss weights requires a lot of tuning, which is time-consuming and labor intensive. To address this problem, we propose an interleaving MTNAS framework, where no tuning of loss weights is needed. In our method, a set of tasks (e.g., A, B, C) are performed in an interleaving loop (e.g., ABCABCABC...) where each task transfers its knowledge to the next task. Each task is learned by minimizing its loss function alone, without intervening with losses of other tasks. Loss functions of individual tasks are organized into a multi-level optimization framework which enables all tasks performed end-to-end. The effectiveness of our method is demonstrated in a variety of experiments.
Ban et al. (Sun,) studied this question.