Some experiment result

I just finished some demo experiments. Originally, I wanted to find some toy example to show task selection's effect in transfer learning. Unfortunately, all the results I found are very disappointing.
Let me summarize the results a little bit:
(1) If the target task has very limited training data, transfer learning do help a lot compared with single task learning.
(2) The tasks selected make a very tiny difference (within 1% percent). Actually, it seems that combine all the tasks together is a very robust and reliable strategy for the data set I'm working on.
(3) Combine all the data together seems always better than transfer learning.


(1) is not surprising and has been approved by existing researchers.
(2) can not justify task selection.
(3) It seems that there's no difference between these tasks in the data set.

Maybe, one interesting problem is to determine whether the data extracted from multiple sources are actually the same. But I feel that's a more difficult problem than task selection.