Another approach to transfer learning in PyTorch is to create a new model and initialize its weights with the pre-trained model's weights. Then, you can train this new model on your specific task and fine-tune the weights as needed.

Transfer learning in PyTorch involves using a pre-trained model as a starting point and fine-tuning it for a different task. You can freeze the parameters of the pre-trained model and only update the weights of the additional layers you add on top.

To implement transfer learning in PyTorch, you can use the torchvision.models module which provides various pre-trained models like ResNet, VGG, and AlexNet. You can load a pre-trained model using torch.hub.load_model() or by directly instantiating the model class.