Transfer learning [256] is a powerful technique that enhances the learning performance of a target task by leveraging knowledge from a related source task. There are two main categories of transfer learning: parameter transfer and sample transfer. In parameter transfer, the learned parameters from the source task are directly copied to the target task’s learning model. In sample transfer, training samples from the source task are integrated into the target task’s dataset and contribute to its training process.