i am fairly new to machine learning, so this may be a silly question. if that is the case, I apologise in advance.
i am training a convolutional neural network on oceanographic images, which include both positive and negative anomalies. The implementation i have tested employs a number of 2D-convolutional layers, followed by max-pooling.
Max-pooling naturally has a bias towards positive values. Does it mean that negative anomalies in my images are going to be given less wight in the training? or is the sign difference accounted for in the convolutional filters?
instinctively, i would like to implement some sort of 'absolute-value max pooling', but since i have found no mention of this in the literature, should i assume this is not needed?