This is a bit of a confusion I have and I never saw any explanation of it, so I have to ask the question. Whenever I see the discussion on how a ConvNNs extracts features, I see something like this: And the discussion that follows is that features are increasingly (hierarchically) more complicated and meaningful, as becomes obvious from the example here. Then, I extracted some layers from a ConvNN (CRF as RNN).There are 64 feature maps in conv1_1, 256 in conv3_1 and 512 in conv5_1. So my first problem is that I can't relate the results I have to the example above. I fail to see how the features become more complicated. All I see is that the image becomes increasingly less distinct. The final output (pixelwise prediction) is very good, so it must work. But I totally don't understand how: all I see is that visible features are increasingly harder to distinguish as feature map size reduces from 1280x768 to 75x50.This is the original image:Conv1_1 Conv3_1Conv5_1
Feature extraction in ConvNNs - layers progression
convnet;feature extraction