Transferring knowledge from pre-trained models can be a powerful technique in PyTorch. One efficient approach is to freeze the weights of the pre-trained layers and only train the newly added layers. This can help in leveraging the knowledge gained from a large dataset without requiring extensive training on a smaller dataset. Another way is to fine-tune the entire model, all the layers including the pre-trained ones, but with a lower learning rate to prevent overfitting.