Model-driven deep-learning.pdf下载

With the arrival of the big data era, data requirements are gradually no longer an obstacle (at least for many areas), but the determination of network topology is still a bottleneck. This is mainly due to the lack of theoretical understandings of the relationship between the network topology and performance. In the current state, the selection of network topology is still an engineering practice instead of scientific research, leading to the fact that most of the existing deep-learning approaches lack theoretical foundations. The difficulties in network design and its interpretation, and a lack of understanding in its generalization ability are the common limitations of the deep-learning approach. These limitations may prevent its widespread use in the trends of ‘standardization, commercialization’ of machine learning and artificial intelligence technology