Multi-Scale and Temporal Transfer Learning for Automatic Tracking of Internal Ice Layers

Pragmatic Deep Learning techniques in recent years have greatly influenced our approaches to data analysis. However, in many real-world problems, even when a large dataset is available, Deep Learning methods have shown less success, for the lack of large labeled dataset, presence of noise, or missing data. In this work, our goal is to track internal ice layers in radar images gathered with various sensors in different years. We will show that transfer learning will not generally work well. However, if the Deep Learning model gets trained on noisy images, there would be a significant improvement. Unlike spatial Transfer Learning, our experiments show that temporal Transfer Learning can provide considerably better results.