I don't have a massive GPU cluster or millions of images. I heard Transfer Learning is the way to go for small teams. Can someone explain how taking a pre-trained model like ResNet or VGG and fine-tuning it works? Is it always better than training a Deep Learning model from scratch, or are there specific risks I should be aware of?
3 answers
Transfer Learning works because the early layers of a Deep Learning model usually learn general features like edges and textures, which are useful for almost any task. By taking a model trained on ImageNet, you "freeze" those early layers and only train the final layers on your specific dataset. This drastically reduces the computational power and data required. It's almost always better for small datasets, though you risk "negative transfer" if your data is fundamentally different from the source data.
When you say "freeze" the layers, how do you decide exactly how many layers to unfreeze when you start the fine-tuning process for a specific Deep Learning task?
It's like standing on the shoulders of giants. You use a model that's already "smart" and just teach it a few new tricks for your specific niche.
Great analogy, Daniel. It turns a month-long training process into a few hours, which is a game-changer for startups in the Deep Learning space.
Richard, it usually depends on how much data you have. If your dataset is very small, you only unfreeze the very last classification layer. If you have a decent amount of data, you can unfreeze more layers starting from the top. The goal is to keep the "knowledge" of the base features while adapting the high-level logic to your specific labels.