Convolutional neural networks (CNNs) use numerous identical replicas of the same network and each layer specializes in one task, the result of which is used in the next layer to solve complex problems.
The advantage of this is that it allows a network to learn a neuron once and use it in numerous places, simplifying the model learning process and thus reducing error. This has made CNNs especially useful in the field of object recognition and image labeling.
CNNs learn increasingly complex and abstract representations. Object recognition in CNNs can start with raw pixel data and learn very specific features, such as edges, basic and complex shapes, patterns and textures.
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