Absolute-value max pooling in 2D convolutional neural networks 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?
If you need to implement such an absolute value max pooling you can convert the output of the convolutional layer to an absolute value and then apply a maxpooling.
Moreover such thought is not necessary as maxpooling works with the output of the convolutional layers and is not influenced by the images directly. By which I mean that the convolutonal layers extract the most important features from the image be it positive or negative that plays a role in predicting a correct output
So for example if a low intensity pixel affects the identification more then it will be given a positive large weight compared to a pixel value that is less influential.