这是我在StackExchange上看到的问题，很有趣：
问题
原文：
There are some variations on how to normalize the images but most seem to use these two methods:
- Subtract the mean per channel calculated over all images (e.g. VGG_ILSVRC_16_layers)
- Subtract by pixel/channel calculated over all images (e.g. CNN_S, also see Caffe's reference network)
The natural approach would in my mind to normalize each image. An image taken in broad daylight will cause more neurons to fire than a night-time image and while it may inform us of the time we usually care about more interesting features present in the edges etc.
Pierre Sermanet refers in 3.3.3 that local contrast normalization that would be per-image based but I haven't come across this in any of the examples/tutorials that I've seen. I've also seen an interesting Quora question and Xiu-Shen Wei's post but they don't seem to support the two above approaches.
What exactly am I missing? Is this a color normalization issue or is there a paper that actually explain why so many use this approach?
大致意思是讲：
关于如何标准化图像有一些变化，但大多数似乎使用这两种方法：
- 减去对所有图像计算的每个通道的平均值（例如 VGG_ILSVRC_16_layers）
- 通过对所有图像计算的像素/通道减去（例如 CNN_S，另见 Caffe 的参考网络）
最自然的方法是对每个图像进行标准化。与夜间图像相比，在光天化日之下拍摄的图像会导致更多的神经元被激发，虽然它可能会告诉我们我们通常关心边缘等中存在的更有趣特征的时间。 这是一个颜色标准化问题还是有一篇论文实际上解释了为什么这么多人使用这种方法？
回答
原文：
Subtracting the dataset mean serves to "center" the data. Additionally, you ideally would like to divide by the sttdev of that feature or pixel as well if you want to normalize each feature value to a z-score.
The reason we do both of those things is because in the process of training our network, we're going to be multiplying (weights) and adding to (biases) these initial inputs in order to cause activations that we then backpropogate with the gradients to train the model.
We'd like in this process for each feature to have a similar range so that our gradients don't go out of control (and that we only need one global learning rate multiplier).
Another way you can think about it is deep learning networks traditionally share many parameters - if you didn't scale your inputs in a way that resulted in similarly-ranged feature values (ie: over the whole dataset by subtracting mean) sharing wouldn't happen very easily because to one part of the image weight w is a lot and to another it's too small.
You will see in some CNN models that per-image whitening is used, which is more along the lines of your thinking.
大致意思是：
我们希望在BackPropagation的过程中每个特征都有一个相似的范围，这样我们的梯度就不会失控（而且我们只需要一个全局学习率）。 您可以考虑的另一种方式是深度学习网络传统上共享许多参数 - 如果您没有以导致类似范围特征值的方式缩放输入（即：通过减去平均值在整个数据集上）共享不会很容易发生，因为图像权重 w 的一部分很大，而另一部分则太小。 你会在一些 CNN 模型中看到使用了每张图像的白化。
