High-Intensified Resemblance and Statistic-Restructured Alignment in Few-Shot Domain Adaptation for Industrial-Specialized Employment

Abstract

The industrial status quo has been changed hastily. Facilities require new technological factors and are less datadriven to make precise inferences at most. Thus, the use of virtual knowledge can be adapted to practical applications to limit human intervention in explanation, namely, comprehensive digital transformation. Deep transfer learning has the role of enabling transferable knowledge and awareness practice of annotating endpoints. However, practical transfer tasks are burdened while considering scarce or non-annotated instances accumulated from external sampling, and these may inevitably degenerate knowledge during training and further degrade performance. In this study, a novel framework composed of the spectral correlation alignment is proposed to enable spectral patterned structure for statistical criterion to diminish the domain disparity under a fewshot alignment and multiple attention orchestration to accelerate a few-shot adaptation. This technique also increases highconcentrated recognition in cross-domain similarity verification. The demonstrations are conducted under public visual adaptation bench-marks and realistic deployment. The empirical experiment illustrates that our approach is better in efficacy and adaptability under data-limited conditions. Besides, in similar realistic applications, it is evident that the proposed scheme is deployable and can be re-practicable to less time as well as data consumption, yet is better in performance.