Skip to main content
. Author manuscript; available in PMC: 2023 Nov 23.
Published in final edited form as: IEEE Trans Image Process. 2022 Nov 23;31:7264–7278. doi: 10.1109/TIP.2022.3221290

TABLE III.

Comparison with competing methods on split datasets (training and testing images are mutually exclusive). For a fair comparison, both SCANMoCo and SPICE used MOCO for feature learning, and ResNet18 as backbone in all training stages. Here the best results for all methods were used for comparison. SPICERes34 used the ResNet34 as backbone. The best two unsupervised results are highlighted in bold.

Method STL10 CIFAR-10 CIFAR-100-20
ACC NMI ARI ACC NMI ARI ACC NMI ARI
ADC [76] 0.530 N/A N/A 0.325 N/A N/A 0.160 N/A N/A
TSUC [29] 0.665 N/A N/A 0.810 N/A N/A 0.353 N/A N/A
NNM [30] 0.808 0.694 0.650 0.843 0.748 0.709 0.477 0.484 0.316
SCAN [1] 0.809 0.698 0.646 0.883 0.797 0.772 0.507 0.486 0.333
RUCSCAN [31] 0.867 N/A N/A 0.903 N/A N/A 0.533 N/A N/A
SCANMoCo [1] 0.855 0.758 0.721 0.874 0.786 0.756 0.455 0.472 0.310
SPICEs 0.862 0.756 0.732 0.845 0.739 0.709 0.468 0.457 0.321
SPICE 0.920 0.852 0.836 0.918 0.850 0.836 0.535 0.565 0.404
SPICE Res34 0.929 0.860 0.853 0.917 0.858 0.836 0.584 0.583 0.422
Supervised 0.806 0.659 0.631 0.938 0.862 0.870 0.800 0.680 0.632