Symmetry in Deep Learning Networks and Its Applications in the Real World

Dear Colleagues,

Deep learning networks have been widely applied in real-world engineering problems. Some effective deep learning networks have typical symmetric structures. For example, the encoder and decoder in U-Net networks have symmetry, as well as the generator and discriminator in GANs. Actual engineering problems are usually based on nonlinear data or models. Nonlinear models typically exhibit symmetry, nonconvexity, and multiple equivalent solutions. Symmetry problems involve the deep integration and clever application of mathematical principles, physical laws, and engineering design. By conducting in-depth research and utilizing the symmetry of deep learning networks, more efficient and powerful deep learning models can be designed to achieve better application results in practical engineering applications.

Dr. Bin Li
Dr. Zhuang Li
Dr. Jiankang Zhang
Guest Editors

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