  Recent breakthroughs of Neural Architecture Search (NAS) extend the field's
research scope towards a broader range of vision tasks and more diversified
search spaces. While existing NAS methods mostly design architectures on a
single task, algorithms that look beyond single-task search are surging to
pursue a more efficient and universal solution across various tasks. Many of
them leverage transfer learning and seek to preserve, reuse, and refine network
design knowledge to achieve higher efficiency in future tasks. However, the
enormous computational cost and experiment complexity of cross-task NAS are
imposing barriers for valuable research in this direction. Existing NAS
benchmarks all focus on one type of vision task, i.e., classification. In this
work, we propose TransNAS-Bench-101, a benchmark dataset containing network
performance across seven tasks, covering classification, regression,
pixel-level prediction, and self-supervised tasks. This diversity provides
opportunities to transfer NAS methods among tasks and allows for more complex
transfer schemes to evolve. We explore two fundamentally different types of
search space: cell-level search space and macro-level search space. With 7,352
backbones evaluated on seven tasks, 51,464 trained models with detailed
training information are provided. With TransNAS-Bench-101, we hope to
encourage the advent of exceptional NAS algorithms that raise cross-task search
efficiency and generalizability to the next level. Our dataset file will be
available at Mindspore, VEGA.
