(a) Data processing including alignment, cropping, and other operations on the raw data to make it applicable to SR tasks. (b) Adaptation to data in multiple scenarios by using various types of SR ...

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Semi-supervised learning merges supervised and unsupervised methods, enhancing data analysis. This approach uses less labeled data, making it cost-effective yet precise in pattern recognition.