# 弱监督学习

工作和学术最大的区别之一就是没有干净的标注数据了，甚至说压根就没有标注数据。

比起深度学习调参，我是更偏向于做数据层面的工作多一点。毕竟调参是真玄学，数据好歹还是有些依据和业务逻辑在里面的。

因此工作上大部分的时间其实是在思考如何构建并清洗数据集，所以弱监督学习模型比起完全的有监督学习在工业领域要有价值的多。


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