解耦表征学习综述

In the era of big data, deep learning has triggered the current rise of artificial intelligence which is known for its ability of efficient autonomous implicit feature extraction. However, the unexplainable "shortcut learning" phenomenon behind it has become a key bottleneck restricting its further development. By exploring the complexity of physical mechanism and logical relationship contained in big data, the disentangled representation learning aims to explore the multi-level and multi-scale explanatory generative latent factors behind the data, and prompts the deep neural network model to learn the ability of intelligent human perception. It has gradually become an important research direction in the field of deep learning, with huge theoretical significance and application value. This article systematically reviews the research of disentangled representation learning, classifies and elaborates state-of-the-art algorithms in disentangled representation learning, summarizes the applications of the existing algorithms and compares the performance of existing algorithms through experiments. Finally, the challenges and research trends in the field of disentangled representation learning are discussed.