Comments (4)
Hi, thanks for your question. In this code (and also in the code of https://github.com/GMvandeVen/continual-learning) I have been using the terms "accuracy" and "precision" interchangeably; with both I mean the same thing. Sorry for the confusion! When I have some time I will try to make the usage of these terms in the code more consistent.
from brain-inspired-replay.
Thanks for quick response. I understand that you have used the terms "accuracy" and "precision" interchangeably. But both have different theoritical meaning.
- Accuracy = (TP+TN)/(TP+FP+FN+TN)
- Precision = TP/(TP+FP)
I just want to confirm which one of accuracy or precision is used in these papers.
from brain-inspired-replay.
Thanks for pointing that out, that is very helpful. Sorry, I realise I should have been more careful with these terms.
The performance measure that is reported in this paper (and also in my other ones) is the proportion of samples classified correctly.
I just updated the code and I now only refer to this measure as accuracy. (I'll update the other repositories as well.)
from brain-inspired-replay.
Thank you so much!
from brain-inspired-replay.
Related Issues (12)
- where i got confuse HOT 1
- command to run baselines HOT 2
- What is the role of replay_mode='current' here HOT 2
- xdg_prop attribute HOT 1
- Allowing an option to reproduce the code but on arbitrary datasets HOT 9
- How to reload model and predict? HOT 2
- Regarding the "reinitialization of networks" HOT 2
- result for 'scenario = class' HOT 2
- Unable to reproduce the results HOT 13
- Can't run cmopare_permMNIST100_bir.py HOT 2
- pre-trained model HOT 2
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from brain-inspired-replay.