Image Clustering On Cifar 100

評価指標

ARI

Accuracy

NMI

Train Set

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

比較表

モデル名	ARI	Accuracy	NMI	Train Set
twin-contrastive-learning-for-online	0.357	0.531	0.529	Train
モデル 2	0.377	0.555	-	Train
multi-modal-deep-clustering-unsupervised	-	0.446	0.418	-
improving-unsupervised-image-clustering-with	-	-	-	Train
information-maximization-clustering-via-multi	0.337	0.49	0.503	-
improving-image-clustering-with-artifacts	0.5053	0.6502	0.771	Test
deep-clustering-for-unsupervised-learning-of	-	0.189	-	Train+Test
spice-semantic-pseudo-labeling-for-image	0.422	0.584	0.583	Train
deep-probability-aggregation-clustering	0.393	0.555	0.542	-
exploring-the-limits-of-deep-image-clustering	0.533	0.671	0.769	Train
joint-unsupervised-learning-of-deep	-	0.137	0.103	Train+Test
representation-learning-for-clustering-via	0.303	0.479	0.468	Train
exploring-the-limits-of-deep-image-clustering	0.612	0.737	0.799	Train
exploring-a-principled-framework-for-deep	-	0.773	0.824	-
let-go-of-your-labels-with-unsupervised-1	0.834	0.898	0.915	-
unsupervised-deep-embedding-for-clustering	-	0.185	0.136	Train+Test
contrastive-clustering	0.266	0.429	0.431	-
clustering-friendly-representation-learning-1	0.264	0.425	0.426	Train
deep-comprehensive-correlation-mining-for	-	0.327	0.285	Train+Test
contrastive-hierarchical-clustering	0.299	0.437	0.467	-
unsupervised-representation-learning-with-1	-	0.151	0.120	Train+Test
information-maximization-clustering-via-multi	0.361	0.519	0.527	Train
deep-clustering-via-probabilistic-ratio-cut	-	0.789	0.856	-
auto-encoding-variational-bayes	-	0.152	0.108	Train+Test
c3-cross-instance-guided-contrastive	0.275	0.451	0.434	-
mitigating-embedding-and-class-assignment	-	0.353	-	-
the-balanced-pairwise-affinities-feature	0.402	0.550	0.560	-
deep-adaptive-image-clustering	-	0.238	0.185	Train+Test
learning-to-classify-images-without-labels	0.333	0.507	0.486	Train
learning-to-classify-images-without-labels	0.301	0.459	0.468	Train