Table 5.

KNN classification result with Resnet18 backbone. In this experiment, we set the number of neighbors as $K=50$ and computed the averaged classification accuracy over three runs.

Similarity Function	$\mathit{\lambda }$	STL10	CIFAR10	CIFAR100	SVHN
TWD (TV)	$0.0$	73.28 ± 0.27	67.03 ± 0.24	25.85 ± 0.39	77.62 ± 0.40
	$0.1$	76.28 ± 0.07	68.60 ± 0.36	26.49 ± 0.24	79.70 ± 0.23
	$0.2$	77.40 ± 0.17	68.48 ± 0.11	25.59 ± 0.16	79.67 ± 0.26
	$0.3$	77.67 ± 0.06	68.26 ± 0.51	24.21 ± 0.35	79.91 ± 0.42