论文标题
Pytorch适应
PyTorch Adapt
论文作者
论文摘要
Pytorch Adapt是一个用于域适应性的库，域适应性是一种机器学习算法，可重新使用现有模型以在新域中工作。它是一个功能齐全的工具包，允许用户在几行代码中创建完整的火车/测试管道。它也是模块化的，因此用户可以仅导入所需的零件，而不必担心被锁定到框架中。该库的一个定义功能是其可自定义性。尤其是，得益于可组合的，懒惰的钩子系统，复杂的训练算法可以轻松修改和组合。在此技术报告中，我们详细说明了这些功能和图书馆的整体设计。代码可从https://www.github.com/kevinmusgrave/pytorch-adapt获得
PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we explain in detail these features and the overall design of the library. Code is available at https://www.github.com/KevinMusgrave/pytorch-adapt
