TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Image Clustering	coil-100	DBC	NMI	0.905	# 7
Image Clustering	coil-100	DBC	Accuracy	0.775	# 5
Image Clustering	Coil-20	DBC	NMI	0.895	# 3
Image Clustering	Coil-20	DBC	Accuracy	0.793	# 3
Image Clustering	MNIST-full	DBC	NMI	0.917	# 7
Image Clustering	MNIST-full	DBC	Accuracy	0.964	# 7
Image Clustering	USPS	DBC	NMI	0.724	# 12
Image Clustering	USPS	DBC	Accuracy	0.743	# 10

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Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders

23 Mar 2017 • Fengfu Li • Hong Qiao • Bo Zhang • Xuanyang Xi

Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance... (read more)

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waynezhanghk/gacluster

linqinghong/Deep-Clustering-Paper

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IMAGE CLUSTERING

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Ranked #3 on Image Clustering on Coil-20

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Image Clustering	coil-100	DBC	NMI	0.905	# 7	Compare
Image Clustering	coil-100	DBC	Accuracy	0.775	# 5	Compare
Image Clustering	Coil-20	DBC	NMI	0.895	# 3	Compare
Image Clustering	Coil-20	DBC	Accuracy	0.793	# 3	Compare
Image Clustering	MNIST-full	DBC	NMI	0.917	# 7	Compare
Image Clustering	MNIST-full	DBC	Accuracy	0.964	# 7	Compare
Image Clustering	USPS	DBC	NMI	0.724	# 12	Compare
Image Clustering	USPS	DBC	Accuracy	0.743	# 10	Compare

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