I am trying to understand this post, but I get confused by the definitions and the differences. What's definition of equivariant?
If I remove all the pooling layers from a CNN, will it make the network to detect features in pixel resolution? For example, detecting the local maximum of a pixel. For example, can a CNN be designed to return True for the following case?
And False for the shifted window:
In the second case it returns false because the 3x3 submatrix isn't centered (yellow dash line) around the local maximum.
Will an architecture that is
from keras.layers import Dense, Conv2D, Flatten model = Sequential() model.add(Conv2D(128, kernel_size=2, activation=’relu’, padding='same', input_shape=(3,3,1))) model.add(Conv2D(64, kernel_size=2, activation=’relu’, padding='same')) model.add(Flatten()) model.add(Dense(10, activation=’softmax’))
be able to differentiate between the tiling of the larger grayscale image?