Comments (2)
Hi @dubowsky ,
Thank you for your interest in our work !
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I check on my side and also obtain 99,88% train accuracy for this run. The reported results in Table 3 was an average accuracy for 5 runs, which might explain the difference. Indeed, the number of errors is important to learn ConfidNet, you should try as much as possible to overfit, adding some regularization might help (see also answer to 2.)
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At the time of the paper, we used an Adam optimizer with LR 1e-3 but you can use any other parameter setting that converge as well, such as SGD with momentum and multi-step decay scheduler. In the end, what will be important is to avoid too much to overfit, in order to have enough errors in training to learn ConfidNet.
Hope this will help for your project :)
Hope
from confidnet.
Clear, thanks so much for your detailed answer!
from confidnet.
Related Issues (14)
- Attempts to pre-train models results in error: UnboundLocalError: local variable 'pred' referenced before assignment HOT 2
- Kernel Restarting HOT 2
- Main Segnet Model for Camvid HOT 1
- Freeze Miniconda version and dependencies in Dockerfile HOT 1
- OOD for? HOT 1
- code bug? why i meet this when i test confidnet? HOT 6
- Question about the accuracy of training vgg16 on CIFAR10 dataset? HOT 6
- How to choose which epoch to use? HOT 2
- CamVid dataset train / val / test split HOT 2
- Trying to understand the ConfidNet training process HOT 1
- ConfidNet Failure Cases & Generalization HOT 3
- ConfidNet performs worse than MCP when I reproduce SVHN results HOT 2
- ModuleNotFoundError HOT 1
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from confidnet.