Table 3.

Robustness to distorted images that occur in the real-world.

Corruption	Backbone	Methods	Accuracy (%)	Specificity (%)	Sensitivity (%)	AUROC
Blur	ResNet	Naive	67.53	83.66	66.92	0.878
		DBA+DRP	76.62	88.31	76.46	0.891
	ResNext	Naive	62.34	81.15	62.00	0.826
		DBA+DRP	72.73	86.43	72.72	0.917
	WideResNet	Naive	66.23	83.01	65.38	0.852
		DBA+DRP	71.43	85.73	71.23	0.873
	DenseNet	Naive	72.73	86.34	72.72	0.858
		DBA+DRP	64.94	82.42	64.67	0.848
	EfficientNet	Naive	51.95	75.83	51.49	0.708
		DBA+DRP	51.95	75.87	51.59	0.714
High Contrast	ResNet	Naive	65.38	82.69	65.38	0.880
		DBA+DRP	78.21	89.10	78.21	0.926
	ResNext	Naive	73.08	86.54	73.08	0.874
		DBA+DRP	74.36	87.18	74.36	0.855
	WideResNet	Naive	66.67	83.33	66.67	0.821
		DBA+DRP	70.51	85.26	70.51	0.868
	DenseNet	Naive	69.23	84.62	69.23	0.848
		DBA+DRP	73.08	86.54	73.08	0.885
	EfficientNet	Naive	56.41	78.21	56.41	0.585
		DBA+DRP	66.67	83.33	66.67	0.827
Low Contrast	ResNet	Naive	73.08	86.54	73.08	0.880
		DBA+DRP	80.77	90.38	80.77	0.927
	ResNext	Naive	74.36	87.18	74.36	0.889
		DBA+DRP	79.49	89.74	79.49	0.919
	WideResNet	Naive	62.82	81.41	62.82	0.856
		DBA+DRP	75.64	87.82	75.64	0.887
	DenseNet	Naive	71.79	85.90	71.79	0.890
		DBA+DRP	78.21	89.10	78.21	0.905
	EfficientNet	Naive	71.79	85.90	71.79	0.896
		DBA+DRP	80.77	90.38	80.77	0.906

For each corruption type and backbone, DBA+DRP outperformed the naive method.