Chapter 12: Transfer Learning and Fine-Tuning in PyTorch
Abstract: Transfer learning and fine-tuning are powerful techniques in PyTorch for leveraging pre-trained models on new, related tasks, especially when limited data or computational resources are available. Transfer Learning: Transfer learning involves using a model pre-trained on a large dataset for a general task (e.g., image classification on ImageNet) as a starting point for a different, but related, task. The idea is that the pre-trained model has already learned rich feature representations that are transferable to the new task. In PyTorch, a common approach is to load a pre-trained model from torchvision.models or other sources. You can then modify the final classification layer to match the number of classes in your new task. Fine-Tuning: Fine-tuning is a specific type of transfer learning where, after replacing the final layer, you continue training the entire model (or parts of it) on your new dataset. This allows the pre-trained weights ...