Comments (4)
Hi @TongLiu-github , thanks for your interest and posting a discussion here.
You are correct. Calibration and bias-variance are two concepts. Calibration uses the expectation over the test set (the overall distribution) while bias-variance uses expectation over different training sets. What we want to say here is that a better calibrated model empirically suggests that the model is less overfitted (but mathematically not related), thus the variance is smaller.
from weighted-soft-label-distillation.
Actually as defined in Eq. 2 in Guo's calibration paper...
@TongLiu-github Hi, sorry to interrupt, Guo's calibration paper
refers to?
from weighted-soft-label-distillation.
@HolmesShuan https://arxiv.org/abs/1706.04599
from weighted-soft-label-distillation.
Thanks~
from weighted-soft-label-distillation.
Related Issues (8)
- 缺少dataset文件 HOT 1
- The pretrained teacher and hyper-parameters on CIFAR-100 HOT 8
- Minor questions on Eq.(2) HOT 5
- Hello, I have a question about training CIFAR-100 HOT 3
- Hi, I cannot reproduce your reported performance on CIFAR-100. HOT 6
- Hyper-parameters settings? HOT 1
- KD Loss keeps raising during training
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from weighted-soft-label-distillation.