I've seen two different approaches for Transfer Learning/Fine Tuning and I'm not sure about their differences and benefits:
- One simply loads the model, eg. Inception, initialized with the weights generated from training on eg. Imagenet, freezes the conv layers and appends some dense layers to adapt to the specific classification task one's working on. Some references are: [1], [2], [3], [4]
- On this keras blog tutorial the process seems more convoluted: runs train/test data through the VGG16 model once and records in two numpy arrays the output from the last activation maps before the fully-connected layers. Then trains a small fully-connected model on top of the stored features (the weights are stored as eg.
mini-fc.h5). At this point if follows a procedure similar to approach #1 where it freezes the first convolutional layers of VGG16 (initialized with weights from imagenet) and trains only the last conv layers and the fully connected classifier (which is instead initialized with the weights from the previous training part of this approach,mini-fc.h5). This final model is then trained. Maybe a more recent version of this approach is explained in the section Fine-tune InceptionV3 on a new set of classes of this keras page: https://keras.io/applications/
What's the difference/benefits of the two approaches? Are those distinct examples of Transfer Learning vs Fine Tuning? The last link is really just a revised version of method #2?
Thanks for your support
