TABLE IV.

More detailed comparison results on STL10. Here all methods were trained and tested on the split train and test datasets respectively. Both the mean and standard deviation results were reported. Each method was conducted five times. Here all methods used the ResNet18 backbone, SCAN_MoCo and SPICE used MoCo for feature learning with STL10 images only. ${\mathrm{SCAN}}_{\mathrm{MoCo}}^{*}$ means no self-labeling.

Method	ACC	NMI	ARI
Supervised	0.806	0.659	0.631
MoCo+k-means	0.797±0.046	0.768±0.021	0.624±0.041
${\mathrm{SCAN}}_{\mathrm{MoCo}}^{*}$	0.787±0.036	0.697±0.026	0.639±0.041
SCANMoCo	0.797±0.034	0.701±0.032	0.649±0.044
SPICEs	0.852±0.011	0.749±0.008	0.719±0.015
SPICE	0.918±0.002	0.849±0.003	0.836±0.002