Hi everyone, can I suggest an approach called Active learning (and Cooperative learning) ?
I’ve heard of it from Dr Michel Valstar in an excellent short video from the youtube channel Computerphile you might already know.
The video : Active (Machine) Learning - Computerphile.
Active learning :
Basically, take a large amount a data you want to label and :
- Do the tedious task of labeling but only for 10% of the data.
- Train a neural net with a data bunch made from this 10%.
- Thanks to inference, use that same neural net to label the other 90% of data
- Intervene only when the neural net gives a low level of certainty and by intervene, I mean confirm or correct the output label(s)
- Add those labeled data to the 10% of already labeled data
Well, let’s assume Active learning is a subset of Cooperative learning.
In active learning, what you inject back are only the output labels from the predictions your neural net had a hard time to make and you had to intervene on.
Cooperative learning is just like above plus you also inject the labeled data that your neural net easily labeled back into the labeled data, without checking.
It is stated in the video that you can expect to spend only 10 to 20% of the time you would normally spend labeling data.
I think it was worth mentioning it.
What do you think of this approach ?
@jeremy I tried so look for active/cooperative learning in the forum but I couldn’t find anything :
Do you think it could be worth a sub wiki article or something ? I would gladdly help