I have been reading about Convolutional neural networks and its use in image recognition. Most of the examples I have seen so far train one single network to classify an image into one label or class. What I have understood so far is that the power of CNN resides in their ability to understand important shapes for each class through the convolution filter. However, my intuition is that if we feed to the network images from different classes the filters will have to adapt its weights to fit them all. (e.g. a filter that is able to recognise dog, cars and humans characteristic shapes)
Wouldn't it be more efficient to have a NN for each label or class as the convolutional filters learned will be more specific and accurate? In this way I can make sure that convolutional filters of the dog model will have their weights optimised to recognise the different parts of the dog.