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Table 2 Results of Cutout Regularization [104], plus denotes using traditional augmentation methods, horizontal flipping and cropping

From: A survey on Image Data Augmentation for Deep Learning

Method C10 C10+ C100 C100+ SVHN
ResNetl8 [5] 10.63 ± 0.26 4.72 ± 0.21 36.68 ± 0.57 22.46 ± 0.31
ResNet18 + cutout 9.31 ± 0.18 3.99 ± 0.13 34.98 ± 0.29 21.96 ± 0.24
WideResNet [21] 6.97 ± 0.22 3.87 ± 0.08 26.06 ± 0.22 18.8 ± 0.08 1.60 ± 0.05
WideResNet + cutout 5.54 ± 0.08 3.08 ± 0.16 23.94 ± 0.15 18.41 ± 0.27 1.30 ± 0.03
Shake-shake regularization [4] 2.86 15.85
Shake-shake regularization + cutout 2.56 ± 0.07 15.20 ± 0.21
  1. A 2.56% error rate is obtained on CIFAR-10 using cutout and traditional augmentation methods
  2. The italic value denote high performance according to the comparative metrics