What are some limitations of transfer learning, and when might it not be effective?

While transfer learning can significantly accelerate training and improve performance, it has its limitations. One major limitation is domain difference; if the source and target domains are too dissimilar, the model may not generalize well, leading to poor performance. Additionally, transfer learning may not be effective if the source model is too small or not sufficiently complex to capture the relevant features of the new task. There’s also the risk of overfitting when using a pre-trained model if the target dataset is small or not representative. Finally, fine-tuning parameters may require careful selection, as inappropriate tuning can lead to negative transfer, where performance worsens instead of improving.