InstaDeep 开源主动学习框架 ALF 覆盖完整数据采集流程
ALF: An Active Learning Framework for Scientific Discovery
InstaDeep 开源了一个主动学习框架 ALF，把数据采集整个流程模块化了，做科研机器学习、标注成本高的朋友可以直接拿去用。
InstaDeep 团队发布开源主动学习框架 ALF，将数据采集流程拆为 5 个模块化组件。ALF 提供统一 API，同时支持两种场景：离线模式可对已有数据集做可复现实验，在线模式可连接 oracle 在真实部署中实时获取新候选样本。该方法适用于标注成本高的领域，如实验、测量或模拟驱动的科研场景。代码已在 GitHub 上开源。
ALF: An Active Learning Framework for Scientific Discovery
Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongst the most promising ways to advance the field. Active learning (AL) offers promise wherever labelling requires expensive experiment, measurement, or simulation. Most existing tools cover only part of the data acquisition loop, and typically focus on either offline benchmarking or online deployment, but not both. We present ALF, a modular AL Framework that runs the full data acquisition loop via five modular components. One clear API for both settings: offline, against an existing dataset for controlled and reproducible experimentation; and online, against an oracle for acquiring new candidates in real-world deployments. ALF is open-source and available at https://github.com/instadeepai/alf.
