1. (2022) Masked Autoencoders Are Scalable Vision Learners;Kaiming;Computer Vision and Pattern Recognition,2022
2. He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 770-778).
3. An end-to-end framework for remaining useful life prediction of rolling bearing based on feature pre-extraction mechanism and deep adaptive transformer model;Su;Computers & Industrial Engineering,2021
4. A deep learning process anomaly detection approach with representative latent features for low discriminative and insufficient abnormal data;Yuan;Computers & Industrial Engineering,2023
5. Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models;Justus;Computers & Industrial Engineering,2023