An alternative approach in transfer learning with PyTorch is to modify the architecture of the pretrained model by replacing or inserting additional layers to adapt it to the new task. This is particularly useful when the input data for the new task differs significantly in terms of size or format from the data the model was originally trained on.

Transfer learning is a powerful technique in computer vision where a pretrained neural network is used as a starting point for a new task. In PyTorch, this can be easily accomplished by loading a pretrained model, freezing the layers and only training the final layers specific to the new task. It saves training time and improves performance, especially when working with limited data.

In addition to freezing the pretrained layers, PyTorch allows us to fine-tune specific layers or even individual neurons if necessary. This can be beneficial when the new task is closely related to the original task the pretrained model was trained on.