I am new to transfer learning and I start by reading A Survey on Transfer Learning, and it stated the following:
according to different situations of labeled and unlabeled data in the source domain, we can further categorize the inductive transfer learning setting into two cases:
case $(a)$ (It is irrelevant to my question).
case $(b): $ No labeled data in the source domain are available. In this case, the inductive transfer learning setting is similar to the self-taught learning setting, which is first proposed by Raina et al. . In the self-taught learning setting, the label spaces between the source and target domains may be different, which implies the side information of the source domain cannot be used directly. Thus, it’s similar to the inductive transfer learning setting where the labeled data in the source domain are unavailable.
From that, I understand that self-taught learning is inductive transfer learning.
But I opened the paper of self-taught learning that was mentioned (i.e paper by Raina et al. .), and It stated the following in the introduction:
Because self-taught learning places significantly fewer restrictions on the type of unlabeled data, in many practical applications (such as image, audio or text classification) it is much easier to apply than typical semi-supervised learning or transfer learning methods.
And here it looks like transfer learning is different from self-taught learning.
So what is the right relation between them?