How do I choose between fine-tuning a pre-trained model and training from scratch?

All answers to this question.

Even with specialized data like MRIs or X-rays, fine-tuning a pre-trained model is usually superior. The early layers of a model trained on ImageNet learn general features like edges, textures, and shapes, which are universal across all images. You should "freeze" these early layers and only train the final "head" of the network on your medical data. This significantly reduces the amount of labeled data required and speeds up convergence. Only consider training from scratch if you have a massive dataset of hundreds of thousands of specialized images and a very high-compute environment.

   Answered 2024-09-17 by Jessica Williams

Transfer learning is powerful, but how are you handling the domain gap? Are you worried that the "weights" learned from colorful natural images might introduce biases when applied to grayscale medical scans, potentially leading to false negatives in your diagnostic predictions?

   Answered 2024-09-19 by David Anderson

• David, that’s a valid concern. To counter that, we use "progressive unfreezing." We start by training just the top layers, then slowly unfreeze the lower layers with a very low learning rate. This allows the model to adapt its feature detectors to the specific textures of medical scans without losing the general knowledge it gained from the original ImageNet training.

     Commented 2024-09-21 by Michael Stevens

Always start with transfer learning as a baseline. It's much faster to iterate on, and you'll know within a few days if the architecture is even capable of picking up the features you need for your specific task.

   Answered 2024-09-23 by Amanda White

• Exactly, Amanda. In my experience, it's rare to beat a fine-tuned SOTA model with a custom scratch-built network unless you have a truly unique input format that standard models can't ingest.

     Commented 2024-09-25 by Jessica Williams