What are some challenges associated with transfer learning?

While transfer learning can significantly enhance model performance, it also comes with several challenges. One major challenge is domain discrepancy; if the source and target tasks share little similarity, the model may not generalize well. Another issue is overfitting on the target dataset, especially if the target dataset is small. Additionally, tuning the model to adapt appropriately to the new task can require nuanced adjustments, both in terms of architecture and learning rates, which can complicate the implementation process.