What are the differences between transfer learning and meta learning? What are the differences between meta-learning and transfer learning?

I have read 2 articles on Quora and TowardDataScience. 


  Meta learning is a part of machine learning theory in which some
  algorithms are applied on meta data about the case to improve a
  machine learning process. The meta data includes properties about the
  algorithm used, learning task itself etc. Using the meta data, one can
  make a better decision of chosen learning algorithm(s) to solve the
  problem more efficiently.


and


  Transfer learning aims at improving the process of learning new tasks
  using the experience gained by solving predecessor problems which are
  somewhat similar. In practice, most of the time, machine learning
  models are designed to accomplish a single task. However, as humans,
  we make use of our past experience for not only repeating the same
  task in the future but learning completely new tasks, too. That is, if
  the new problem that we try to solve is similar to a few of our past
  experiences, it becomes easier for us. Thus, for the purpose of using
  the same learning approach in Machine Learning, transfer learning
  comprises methods to transfer past experience of one or more source
  tasks and makes use of it to boost learning in a related target task.


The comparisons still confuse me as both seem to share a lot of similarities in terms of reusability. Meta-learning is said to be "model agnostic", yet it uses metadata (hyperparameters or weights) from previously learned tasks. It goes the same with transfer learning, as it may reuse partially a trained network to solve related tasks. I understand that there is a lot more to discuss, but, broadly speaking, I do not see so much difference between the two.

People also use terms like "meta-transfer learning", which makes me think both types of learning have a strong connection with each other.

I also found a similar question, but the answers seem not to agree with each other. For example, some may say that multi-task learning is a sub-category of transfer learning, others may not think so.
Meta-learning is more about speeding up and optimizing hyperparameters for networks that are not trained at all, whereas transfer learning uses a net that has already been trained for some task and reusing part or all of that network to train on a new task which is relatively similar. So, although they can both be used from task to task to a certain degree, they are completely different from one another in practice and application, one tries to optimize configurations for a model and the other simply reuses an already optimized model, or part of it at least.