swa_gaussian
swa_gaussian copied to clipboard
wjmaddox
reliability diagrams
Hi Wesley (@wjmaddox) I was wondering if you could shed some light on the calibration plots. So I'm running the save calibration plots script giving as input the predictions and targets of vgg16 trained on cifar10(5+5) but when I plot the output I get something not even close to the plots in the paper. From my reading it seems that both sgd and swag are under confident. Am I doing something wrong?
Hi Kirk,
You're reading the plot incorrectly - beneath the blue line shows that both SGD and SWAG are overconfident in that situation (confidence > accuracy). With that being said, I'm not sure if we ever checked calibration of the CIFAR5+5 task - will get back to you on that.
Just following up, I checked and we never seem to have run calibration on CIFAR 5+5, but it's not terribly surprising that both SGD and SWAG (somewhat less so) are overconfident here as well.
Hi Wesley,
thanks a lot for the follow up.
May I ask an additional question if you could clarify that for me please?
Why is the split on cifar10 (5+5) deterministic, (i.e. predefined as 0<---first half of the classes and 1<---the remaining, where 0 = [0, 1, 2, 8, 9] and 1 = [3, 2, 4, 8, 1] <--- labels)
Have you noticed that if you train on 1 yields better results than on 0 for out of distribution?
I believe we sampled those randomly at one point, so it's a holdover from that.
No, I haven't noticed that.
Thank you, Wesley!
Here's an example of the difference between sgd vs swag if you train on 1 vs 0. Basically swag seems to perform worse than sgd when trained on 0. Left plots are trained on 1 and right ones on 0.
