Table 3.

KNN classification result with Resnet18 backbone. In this experiment, we set the number of neighbors as $K=10$ and computed the averaged classification accuracy over three runs. Note that the Wasserstein distance with ($\mathit{B}={\mathit{I}}_{d_{\mathrm{out}}}$) is equivalent to a total variation.

Similarity	Prob Model	STL10	CIFAR10	CIFAR100	SVHN
Cosine	N/A	75.44 ± 0.21	66.96 ± 0.45	31.63 ± 0.25	74.71 ± 0.31
	Softmax	71.25 ± 0.30	63.80 ± 0.48	26.18 ± 0.36	73.06 ± 0.47
	SEM	71.34 ± 0.31	61.26 ± 0.42	25.40 ± 0.06	73.41 ± 0.95
	AF (DCT)	72.15 ± 0.53	65.52 ± 0.45	24.93 ± 0.24	75.68 ± 0.13
TWD (TV)	Softmax	63.42 ± 0.24	59.03 ± 0.58	24.95 ± 0.31	70.87 ± 0.29
	SEM	63.72 ± 0.17	55.57 ± 0.35	23.40 ± 0.36	71.69 ± 0.75
	AF	63.97 ± 0.05	59.96 ± 0.44	25.29 ± 0.17	73.44 ± 0.35
	AF (PE)	71.04 ± 0.37	64.28 ± 0.14	25.71 ± 0.20	75.70 ± 0.42
	AF (DCT)	72.75 ± 0.11	67.01 ± 0.03	24.95 ± 0.17	76.98 ± 0.44
	Softmax + JD	72.05 ± 0.30	66.61 ± 0.20	26.91 ± 0.19	76.65 ± 0.56
	SEM + JD	70.73 ± 0.89	62.75 ± 0.61	24.83 ± 0.27	74.71 ± 0.43
	AF + JD	71.74 ± 0.19	66.74 ± 0.20	26.68 ± 0.35	77.10 ± 0.04
	AF (PE) + JD	74.10 ± 0.20	66.82 ± 0.36	26.17 ± 0.00	77.55 ± 0.50
	AF (DCT) + JD	76.24 ± 0.22	68.62 ± 0.40	25.70 ± 0.14	79.28 ± 0.22
TWD (Clust)	Softmax	67.95 ± 0.42	61.59 ± 0.29	23.34 ± 0.26	73.88 ± 0.05
	SEM	72.43 ± 0.11	63.63 ± 0.42	21.29 ± 0.28	77.04 ± 0.77
	AF	69.09 ± 0.05	62.49 ± 0.45	22.56 ± 0.25	74.31 ± 0.40
	AF (PE)	72.08 ± 0.07	64.56 ± 0.31	22.51 ± 0.29	75.98 ± 0.23
	AF (DCT)	71.64 ± 0.15	65.51 ± 0.36	21.04 ± 0.10	77.59 ± 0.25
	Softmax + JD	73.07 ± 0.13	66.38 ± 0.27	23.97 ± 0.11	76.82 ± 0.50
	SEM + JD	75.50 ± 0.15	67.44 ± 0.10	21.90 ± 0.19	78.91 ± 0.30
	AF + JD	72.70 ± 0.08	66.12 ± 0.26	23.50 ± 0.21	76.92 ± 0.06
	AF (PE) + JD	73.66 ± 0.47	66.58 ± 0.01	22.86 ± 0.02	77.44 ± 0.30
	AF (DCT) + JD	73.79 ± 0.12	67.34 ± 0.38	21.96 ± 0.34	78.00 ± 0.60