README.md

Uncertainty in classification

Dataset

Name	Known class	Unknown class
Cifar 100	0~49 indices	50~99 indices
Cifar 10	0~4 indices	5~9 indices

Hyperparameter

• Optimizer
  • SGD, lr=0.4, momentum=0.9, weight_decay=1e-4
  • Warmup_epoch=5, warmup_factor=0.1
  • Cosine LR decay
• Hyperparameters
  • Batch size: 128
  • Input size: 32x32
  • Epoch: 300
  • Num. of ensemble: 5
  • Num. of infer: 10
• Regularizer
  • Augmentation: RandomCrop, HorizontalFlip
  • Mixup alpha: 0.2
  • Label smoothing: 0.1
  • Early stopping
• Scratch Learning(without ImageNet pretrained weights)

Experiments

• Split the half of classes as unknown data. and some portion of known data are test set.
• Train with known data and get the quantitative results(accuracy, ece) from test set.
• Get the qualitative result(confidences) from test set, rotated test set and unkwnon data.

Cifar 100

Method	Accuracy(train)	Accuracy(test)	ECE
Softmax	0.9297	0.6534	0.0595
Temp. scaling	0.9267	0.6386	0.1385
MC Dropout	0.9042	0.6778	0.0715
Ensemble	0.9361	0.6866	0.0822

Table 1. Result of ResNet18

Fig1. Distribution of confidence of the test set. ResNet18

Fig2. Distribution of confidence of the test set that rotated in 90 degree. ResNet18

Fig3. Distribution of confidence of the unknown data. ResNet18

Method	Accuracy(train)	Accuracy(test)	ECE
Softmax	0.9326	0.6636	0.0796
Temp. scaling	0.9288	0.6402	0.1555
MC Dropout	0.9360	0.7063	0.0750
Ensemble	0.9418	0.7006	0.0725

Table 2. Result of ResNet50

Fig4. Distribution of confidence of the test set. ResNet50

Fig5. Distribution of confidence of the test set that rotated in 90 degree. ResNet50

Fig6. Distribution of confidence of the unknown data. ResNet50

Cifar 10

Method	Accuracy(train)	Accuracy(test)	ECE
Softmax	0.9854	0.9176	0.0870
Temp. scaling	0.9821	0.9092	0.0506
MC Dropout	0.9832	0.9144	0.0967
Ensemble	0.9872	0.9320	0.1044

Table 3. Result of ResNet18

Fig7. Distribution of confidence of the test set. ResNet18

Fig8. Distribution of confidence of the test set that rotated in 90 degree. ResNet18

Fig9. Distribution of confidence of the unknown data. ResNet18

Method	Accuracy(train)	Accuracy(test)	ECE
Softmax	0.9856	0.9286	0.1028
Temp. scaling	0.0006	0.0036	0.1980
MC Dropout	0.9849	0.9224	0.1098
Ensemble	0.9873	0.9368	0.1071

Table 4. Result of ResNet50

Fig10. Distribution of confidence of the test set. ResNet50

Fig11. Distribution of confidence of the test set that rotated in 90 degree. ResNet50

Fig12. Distribution of confidence of the unknown data. ResNet50

Conclusion

1. Bigger model shows more overconfidence.
2. Softmax is still good function but ensemble is better option.
3. Dropout is also good method but too much resource need to inference.
4. Temperature scaling shows worse result.

Citation

[Temperature scaling] 'On Calibration of Modern Neural Networks'
[MC Dropout] 'Dropout as a Bayesian Approximation_Representing Model Uncertainty in Deep Learning'
[Ensemble] 'Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles'