In a convolutional neural network (CNN), the layer weights are learnt such that they extract meaningful features from the data. For each layer, can we merge multiple filters into a single filter after the CNN is trained?
Example: Suppose the first layer of a CNN has N (3x3) filters. After training, how can we merge some filters out of these into a single filter and discard the individual filters, so that the merged filter will help to extract the combined features. This might need retraining the network after merging is done. This might help to reduce the number of filters in the network and may speed up inference.
Can anyone suggest some techniques using which filters can be merged?