DataScience- Teacher Student Model Semi-supervised

在 Billion-scale semi-supervised learning for image classification（Facebook AI Research)
當中有提到student model不將D與D-hat合起來train的原因：
Remark: It is possible to use a mixture of data in D and Dˆ for training like
in previous approaches [34]. However, this requires for searching for optimal
mixing parameters, which depend on other parameters. This is resource-intensi
ve in the case of our large-scale training. Additionally, as shown later in ou
r analysis, taking full advantage of large-scale un- labelled data requires ad
opting long pre-training schedules, which adds some complexity when mixing is
involved.

不太確定第一個原因searching for mixing parameters指的是？

及第二個原因 D+D-hat不是在training student model前就準備好了嗎？
為何會增加complexity

謝謝大家

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