AutoAugment: Learning Augmentation Policies from Data

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Fine-Grained Image Classification Caltech-101 AutoAugment Top-1 Error Rate 13.07% # 5
Image Classification CIFAR-100 PyramidNet+ShakeDrop Percentage correct 89.3 # 10
Fine-Grained Image Classification FGVC Aircraft AutoAugment Top-1 Error Rate 7.33 # 2
Accuracy 92.67% # 15
Fine-Grained Image Classification Oxford 102 Flowers AutoAugment Top-1 Error Rate 4.64% # 4
Accuracy 95.36% # 13
Fine-Grained Image Classification Oxford-IIIT Pets AutoAugment Top-1 Error Rate 11.02% # 9
Accuracy 88.98% # 13
Fine-Grained Image Classification Stanford Cars AutoAugment Accuracy 94.8% # 10
Image Classification SVHN AutoAugment Percentage error 1.02 # 3

Methods used in the Paper


METHOD TYPE
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
LSTM
Recurrent Neural Networks
AutoAugment
Image Data Augmentation