One-Shot Neural Architecture Search for Deep Multi-Task Learning in Computer Vision

Sammanfattning: In this work, a neural architecture search algorithm for multi-task learning is proposed. Given any dataset and tasks group, the method aims to find the optimal way of sharing layers among tasks in convolutional neural networks. A search space suited to multi-task learning is designed, and a novel strategy to rank different Pareto optimal solutions is developed. The core of the algorithm is an adaptation of a state-of-the-art neural architecture search strategy. Experimental results on the Cityscapes dataset, on the tasks of semantic segmentation and depth estimation, do not provide the expected results. Despite the lack of stable results, this work lays down the fundamentals to further develop novel multi-task neural architecture search methods.