Transfer learning is great, but is still pretty limited; the main success stories are retraining CNNs from ImageNet to other datasets and word2vec/GloVE. In th

Transfer learning is great, but is still pretty limited; the main success stories are retraining CNNs from ImageNet to other datasets and word2vec/GloVE. In the space of all the things you would want to do with ML, this is pretty limited. [EDIT]: I stumbled upon a transfer learning for NER paper a second ago: https://arxiv.org/abs/1705.06273v1 https://arxiv.org/abs/1705.06273v1 And the results are not conclusive. You can can improve your results in truly data impoverished regimes, but you get diminishing returns as you add more data, so the only way to catch up is to get more data. So, you may be able to get reasonable performance with fewer samples, but you will still get better performance with more samples, and the more samples you get, the less benefit you get from transfer learning. This may not always hold, and I wonder if active learning paired with transfer learning would do better, but at the moment it's not convincing.