I'm new to the CS stack exchange, so a fond hello to you all!
I joined since I have a question I've been curious about. I have recently been running some experiments in transfer learning - specifically I train a network to recognise a certain image, let's say a cat, and then I transfer the input and hidden layers of the network to another dataset and see if the trained weight distribution is a better starting point as opposed to a random distribution through XAVIER etc. etc.
I now wonder, what if the input is different, rather than the output? For example, if I have a dataset of Hotel reviews and build a sentiment analysis type predictor that would predict whether a review is negative or positive, one would have a binary classification problem. If you were then to apply those layers and weights as the starting point to Restaurant reviews, one has changed the input attributes (eg. word stems from a different domain) but the output may still be a binary classification problem for positive and negative classes.
All of the examples of transfer learning I see will change the input and output layers while retaining the hidden layers. If the input changes, but the output does not, is it still Transfer Learning?