An obvious limitation of general machine learning algorithms is that they depend heavily on the representations of the given data and usually fail to give useful predictions when the input 𝒙 is intricate, such as image, audios, videos, etc. . To address this issue, instead of directly learning a mapping from the input space to the output space, it is necessary to learn the representation itself, and this is precisely where DL demonstrates its superiority. Over the past few decades, DL models have been extensively used in the field of computer vision , natural language processing (55-57), causal inference , healthcare , and environmental sciences (65-67).