  Deep transfer learning techniques try to tackle the limitations of deep
learning, the dependency on extensive training data and the training costs, by
reusing obtained knowledge. However, the current DTL techniques suffer from
either catastrophic forgetting dilemma (losing the previously obtained
knowledge) or overly biased pre-trained models (harder to adapt to target data)
in finetuning pre-trained models or freezing a part of the pre-trained model,
respectively. Progressive learning, a sub-category of DTL, reduces the effect
of the overly biased model in the case of freezing earlier layers by adding a
new layer to the end of a frozen pre-trained model. Even though it has been
successful in many cases, it cannot yet handle distant source and target data.
We propose a new continual/progressive learning approach for deep transfer
learning to tackle these limitations. To avoid both catastrophic forgetting and
overly biased-model problems, we expand the pre-trained model by expanding
pre-trained layers (adding new nodes to each layer) in the model instead of
only adding new layers. Hence the method is named EXPANSE. Our experimental
results confirm that we can tackle distant source and target data using this
technique. At the same time, the final model is still valid on the source data,
achieving a promising deep continual learning approach. Moreover, we offer a
new way of training deep learning models inspired by the human education
system. We termed this two-step training: learning basics first, then adding
complexities and uncertainties. The evaluation implies that the two-step
training extracts more meaningful features and a finer basin on the error
surface since it can achieve better accuracy in comparison to regular training.
EXPANSE (model expansion and two-step training) is a systematic continual
learning approach applicable to different problems and DL models.
