Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

This dataset contains code and data for our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Active Learning problems. The code is also available on GitHub. Information on the code can be found in the file README.md and in the Jupyter notebooks in the examples folder. Additionally, we provide the files results.tar.gz and plots.tar.gz which contain generated data and plots. These files can be unpacked in folders specified in custom_paths.py (see README.md) and can be used as described in examples/benchmark.ipynb.

本数据集汇聚了论文《深度批量主动学习回归的框架与基准》的相关代码与数据。该代码可用于复现论文中的实验结果、对新兴方法进行基准测试，或将所提出的方法应用于新的深度批量主动学习问题。此外，代码亦可在GitHub平台获取。有关代码的详细信息可于README.md文件及示例文件夹中的Jupyter笔记本中查阅。同时，我们提供results.tar.gz和plots.tar.gz文件，其中包含生成数据和图表。用户可按照custom_paths.py文件中指定的文件夹进行解压缩，并参照examples/benchmark.ipynb中的说明使用这些文件。