Inquiry about Pre-trained Model & Parameter Setup about pycil HOT 4 CLOSED

Thanks for your interest.

For the first problem, I guess you might confuse the average accuracy with the last session's accuracy. We report the average accuracy of all sessions, which is also the benchmark in most class-incremental learning papers, while your table seems to be the accuracy of the last stage.

For the second problem, different methods have different suitable parameters. For example, lwf and ewc may need smaller weight decay while icarl requires a larger one. We set slightly different parameters between different methods to fully reflect the performance of these methods.

from pycil.

Thanks for your answer!

Could I confirm my understanding again for the first question because it does differ from another continual learning library I am working on : )

For my first question, is that means:
On task 0, we get accuracy0 on class 0-9;
on task 1, we get accuracy1 on class 0-19;
on task 2, we get accuracy2 on class 0-29;
...
on task 9, we get accuracy9 on class 0-99;
then we get the average accuracy of (accuracy0, accuracy1 ... accuracy9)?

from pycil.

You're right.

from pycil.

Got it. Thanks.

from pycil.