论文标题
Shift：一种高效，灵活的搜索引擎，用于转移学习
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
论文作者
论文摘要
转移学习可以看作是从头开始的数据和计算效率替代培训模型的替代方法。丰富的模型存储库（例如Tensorflow Hub）的出现使从业者和研究人员能够在各种下游任务中释放这些模型的潜力。随着这些存储库不断成倍增长，有效地为手头任务选择一个好的模型变得至关重要。通过仔细比较各种选择和搜索策略，我们意识到，没有一种方法优于其他方法，而混合或混合策略可以是有益的。因此，我们提出了Shift，这是用于转移学习的第一个下游任务感知，灵活和有效的模型搜索引擎。这些属性由自定义查询语言shift-ql以及基于成本的决策者以及我们在经验上验证的基于成本的决策者启用。在机器学习开发的迭代性质的推动下，我们进一步支持对查询的有效逐步执行，这需要与我们的优化共同使用时进行仔细的实施。
Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enables practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. By carefully comparing various selection and search strategies, we realize that no single method outperforms the others, and hybrid or mixed strategies can be beneficial. Therefore, we propose SHiFT, the first downstream task-aware, flexible, and efficient model search engine for transfer learning. These properties are enabled by a custom query language SHiFT-QL together with a cost-based decision maker, which we empirically validate. Motivated by the iterative nature of machine learning development, we further support efficient incremental executions of our queries, which requires a careful implementation when jointly used with our optimizations.
