With their pooling layers that can generate multiple levels of representations corresponding to different levels of abstraction, they can coalesce the information from local and global receptive fields. In their cascaded layers that can contain hundreds of millions of parameters, they can model highly nonlinear functions. There are many compelling advantages of deep-learning methods. An illustration of common deep-learning networks such as convolutional neural networks, autoencoders, and generativeadversarial networks (GANs) can be seen in Figure 1, and a very comprehensive discussion of different deep-learning techniques for visual understanding also can be found in the tutorial articles in the first part of this special issue in the November 2017 issue of IEEE Signal Processing Magazine (SPM). Ascribing to their complex and layered structures, a broader family of data-driven machine-learning methods based on neural network models today is called deep learning. On the journey of developing algorithms that can match human visual perception, most of the progress happened within the last decade with the rebirth of artificial neural networks in computer vision, in particular, convolutional architectures.
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