Global optimal semi-supervised learning for single-pixel image-free sensing

Single-pixel sensing offers low-cost detection and reliable perception, and the image-free sensing technique enhances its efficiency by extracting high-level features directly from compressed measurements. However, the conventional methods have great limitations in practical applications, due to their high dependence on large labelled data sources and incapability to do complex tasks. In this Letter, we report an image-free semi-supervised sensing framework based on GAN and achieve an end-to-end global optimization on the part-labelled datasets. Simulation on the MNIST realizes 94.91% sensing accuracy at 0.1 sampling ratio, with merely 0.3% of the dataset holding its classification label. When comparing to the conventional single-pixel sensing methods, the reported technique not only contributes to a high-robust result in both conventional (98.49% vs. 97.36%) and resource-constrained situations (94.91% vs. 83.83%) but also offers a more practical and powerful detection fashion for single-pixel sensing, with much less human effort and computation resources.