Sig2Sig: Signal Translation Networks to Take the Remains of the Past

SangYeon Kim, Hyunwoo Lee, Jonghee Han, Joon-Ho Kim

Abstract: In these days, more and more deep learning models are applied to various tasks in an industrial area. Because sensors are changed rapidly, if we want to use the model then we need to collect new sensor data and train with them again. Moreover, sometimes we need to tune the model for the task and adjust the hyperparameters. In this paper, we propose a signal translation networks, Sig2Sig, that converts from the new sensor signals to old ones in order to reuse the past model, which was trained on plenty of old sensor signals. We only need a small paired-dataset for training Sig2Sig and then translated signals from a new sensor can be classified by the past classification model almost without performance degradation. To do such a robust translation, our model is based on a modified u-net with squeeze and excitation networks, instance normalization, and with attention parts for robust outputs. We also use various loss functions which include uncertainty loss for ignoring noise parts and focusing on important parts of signal images. Our results show that not only translated signal is realistic and similar to target data but also the past classification model can be reused on the new sensor domain with almost similar performance as the old sensor.