Transfer Learning Approach for Improved Fiber Event Recognition with limited State of Polarization Data

We present an innovative transfer learning method for classifying risky events with scarce state of polarization (SOP) data, utilizing a deep convolutional neural network pre-trained on unrelated images. Achieving a 96.3% accuracy on just 400 samples, this approach offers a robust solution for data-limited scenarios.