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A well balanced sensitivity for lengthy-array headshots. Way too substantial a sensitivity may well enable it to be challenging to Manage the scope, though way too minimal could hinder brief adjustments.

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就是先让不同的specialist单独计算loss，然后再加权求和得到总体的decline。这意味着，每个specialist在处理特定样本的目标是独立于其他professional的权重。尽管仍然存在一定的间接耦合（因为其他specialist权重的变化可能会影响门控网络分配给qualified的rating）。如果门控网络和professional都使用这个新的decline进行梯度下降训练，系统倾向于将每个样本分配给一个单一specialist。当一个professional在给定样本上的的decline小于所有expert的平均decline时，它对该样本的门控score会增加；当它的表现不如平均decline时，它的门控score会减少。这种机制鼓励expert之间的竞争，而不是合作，从而提高了学习效率和泛化能力。下面是一个示意图：

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