Sensor transfer learning, specifically SensorTalk3, enhances accuracy by achieving a low Mean Absolute Percentage Error (MAPE) of 1.738% compared to the original sensor's 7.792% error, showcasing significant improvement.
传感器迁移学习，特别是 SensorTalk3，通过实现较低的平均绝对百分比误差（MAPE）1.738%，相较于原始传感器的 7.792%误差，展示了显著的改进。

Transfer learning in the sensor enhances accuracy, reducing temperature prediction error to 0.14°C and humidity to 0.42%, surpassing traditional deep learning methods in multi-wavelength laser absorption spectroscopy.
传感器中的迁移学习提升了准确性，将温度预测误差降至 0.14°C，湿度误差降至 0.42%，在多波长激光吸收光谱学中超越了传统深度学习方法。

ChemVise proposes a novel transfer learning approach using molecular semantics for accurate chemical sensor signal classification, enhancing accuracy without exhaustive real-world experiments.
ChemVise 提出了一种利用分子语义的新型迁移学习方法，用于精确的化学传感器信号分类，无需大量现实世界实验即可提高准确性。