What is the relation between self-taught learning and transfer learning?

Question

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. [22]. 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. [22].), 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?

Answer 1

After a lot of searches, I think self-taught learning is a Transfer learning category, I think when Self-taught learning paper published (2007), there isn't any good survey on transfer learning, and as seen by high citation of the paper of Pan, his paper (which published in 2009) describes transfer learning in a clear way that did not exist before it.

Also, it is reasonable to consider Self-Taught learning a transfer learning category because he actually transfers the knowledge learned from unlabelled data to use it in the supervised task that we want to do (there is no need for the unlabeled data (that used for training) to follow the same class labels or generative distribution as the labelled data that will be used for the supervised task).

If someone could find something wrong with my answer or there is something missing, then please tell me.