不取决于地面真相的损失功能
据我所知，机器学习环境中的损失函数通常是两个变量的函数：预测和地面真相。是否有不取决于地面真相的损失功能？对于一个简单的示例，如果我预测一个实值变量，则可以将均方根误差用作损耗函数。但是，如果我以物理的理由知道输出描述了一些正数，那么我可以定义一个主要是MSE的自定义损失函数，但也许有一个额外的术语，每次做出负面预测时都会受到惩罚。这个额外的术语不必考虑地面真相的价值。这种想法普遍吗？有某种名字吗？
From what I know, the loss function in machine learning context is usually a function of two variables: the prediction and the ground truth. Is there such thing as a loss function that does not depend on the ground truth? For a simple example, if I'm predicting a real-valued variable, I can use a mean squared error as the loss function. But if I know, on physical grounds, that the output describes some positive quantity, I can define an improved custom loss function that is mainly the MSE, but maybe has an additional term that penalizes every time a negative prediction is made. This additional term need not take into account the value of the ground truth. Is this kind of idea prevalent? Does it have some kind of name?
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损失功能不必依赖地面真理。从字面上看，这是您最小化的任何东西，这可以是每点与地面真理的比较，它可以验证某些属性（例如您所描述的），它可以是正规化（例如Actiavtions的规范），也可以取决于总体上统计数据（例如损失鼓励对两个不同点的预测不同）。这里没有“规则”，从字面上看，您要最小化的所有内容都是损失函数。
Loss function does not have to depend on ground truth. It is literally anything that you minimize, which can be per-point comparison to ground truth, it can be verification of some property (like what you describe) it can be a regularisation (e.g. norm of actiavtions), or it can depend on overall statistics (e.g. loss encouraging predictions for two different points to be different). There are no "rules" here, literally everything you want to minimize is by definition, a loss function.