patchwise training什么意思

patchwise training什么意思

参考这个回答吧，贴出来，未翻译。网页链接
> The term "Fully Convolutional Training" just means replacing fully-connected layer with convolutional layers so that the whole network contains just convolutional layers (and pooling layers).> The term "Patchwise training" is intended to avoid the redundancies of full image training. In semantic segmentation, given that you are classifying each pixel in the image, by using the whole image, you are adding a lot of redundancy in the input. A standard approach to avoid this during training segmentation networks is to feed the network with batches of random patches (small image regions surrounding the objects of interest) from the training set instead of full images. This "patchwise sampling" ensures that the input has enough variance and is a valid representation of the training dataset (the mini-batch should have the same distribution as the training set). This technique also helps to converge faster and to balance the classes. In this paper, they claim that is it not necessary to use patch-wise training and if you want to balance the classes you can weight or sample the loss. In a different perspective, the problem with full image training in per-pixel segmentation is that the input image has a lot of spatial correlation. To fix this, you can either sample patches from the training set (patchwise training) or sample the loss from the whole image. That is why the subsection is called "Patchwise training is loss sampling". So by "restricting the loss to a randomly sampled subset of its spatial terms excludes patches from the gradient computation." They tried this "loss sampling" by randomly ignoring cells from the last layer so the loss is not calculated over the whole image.

patchwise training补丁明智的训练的意思

patch wise updater 明智的补丁更新

介于image training和pixel training之间的训练，即在语义分割任务中，将从原图片中随机crop多个子图进行训练。