Circumventing Outliers of AutoAugment with Knowledge Distillation

AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization. This paper delves deep into the working mechanism, and reveals that AutoAugment may remove part of discriminative information from the training image and so insisting on the ground-truth label is no longer the best option... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Classification ImageNet KDforAA (EfficientNet-B7) Top 1 Accuracy 85.5% # 14
Number of params 66M # 23
Image Classification ImageNet KDforAA (EfficientNet-B8) Top 1 Accuracy 85.8% # 12
Number of params 88M # 14

Methods used in the Paper