单个网络 - 多个输出，还是多个网络 - 单输出？
在设计具有多个输出的前馈神经网络时，具有多个输出的单个网络与每个具有单个输出的多个网络之间是否存在概念上的差异（除了计算效率之外）？
尽管同一网络中的输出神经元不会“即时”相互影响，但它们确实会影响训练，因为每个输出的误差都会反向传播并影响隐藏层的权重，进而影响其他输出的值。
是否有任何问题都可以更好地解决？直观地说，我想说单个网络更适合一次只有一个输出应该处于活动状态的问题（即 OCR），而多个网络更适合于多个输出可以同时处于活动状态的问题（即当每个输出对应于某个特征时）在输入中，其中几个可以同时出现）。但这只是直觉。它实际上成立吗？
When designing a feed forward neural network with multiple outputs, is there a conceptual difference (other than computational efficency) between having a single network with multiple outputs, and having multiple networks, each having a single output?
Although output neurons in the same network don't affect each other "on the fly", they do affect training, since the error from each output backpropagates and affects the weights of the hidden layer, which in turn affect the value of the other outputs.
Are there problems that are better solved either way? Intuitively, I'd say that a single network better fits problems where only one output should be active at a time (i.e OCR), where multiple networks better fit problems where multiple outputs can be active simultanously (i.e. when each output correspond to some characteristic in the input, where several of them can be present simultaniously). But this is mere intuition. Does it practically hold?
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正如您自己指出的，概念上的差异是联合训练与单独训练。我认为人们发现在大多数情况下，如果问题相关（例如，如果所有输出都是关于识别字符），联合训练会有所帮助。所以我认为，在你提到的这两个问题中，联合训练都会有所帮助，无论多个输出是否可以同时激活。
联合训练无济于事并且可能会造成伤害的情况是任务根本不相关，例如，如果一个输出与“字符 a”有关，而另一个输出与“声音 x”有关。
The conceptual difference, as you noted yourself, is joint training vs. separate training. I think people find that in most cases joint training helps if the problems are related (e.g. if all outputs are about recognizing characters). So I'd think that that in both problems you mention joint training will help, regardless of whether multiple outputs can be active simultaneously.
A case where joint training wouldn't help and will probably hurt is when the tasks are not related at all, e.g. if one output is about 'character a' and another one is about 'sound x'.