I’ve typically only performed transfer learning via fine-tuning with neural networks (eg. image classifiers from pre-trained MobileNet, etc.), but does the same idea hold for a model like logistic regression or CRF? I’d argue yes because your essentially just training a new model with non-randomized initial weights (a prior). But am I missing something?

I’m currently looking into cross-domain transfer learning for non-neural NER models, and I wanted to fine-tune the weights of a pre-trained CRF with some newly annotated user-generated data.