TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Image Clustering	CIFAR-10	DeepCluster	Accuracy	0.374	# 24
Image Clustering	CIFAR-10	DeepCluster	NMI	-	# 26
Image Clustering	CIFAR-10	DeepCluster	Train set	Train+Test	# 1
Image Clustering	CIFAR-10	DeepCluster	ARI	-	# 26
Image Clustering	CIFAR-10	DeepCluster	Backbone	ResNet-34	# 1
Image Clustering	CIFAR-100	DeeperCluster	Accuracy	0.189	# 19
Image Clustering	CIFAR-100	DeeperCluster	Train Set	Train+Test	# 1
Unsupervised Semantic Segmentation	Cityscapes test	MDC	mIoU	7.1	# 10
Unsupervised Semantic Segmentation	Cityscapes test	MDC	Accuracy	40.7	# 10
Unsupervised Semantic Segmentation	ImageNet-S-50	MDC (Supervised pretrain)	mIoU (val)	14.6	# 5
Unsupervised Semantic Segmentation	ImageNet-S-50	MDC (Supervised pretrain)	mIoU (test)	14.3	# 5

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Deep Clustering for Unsupervised Learning of Visual Features

ECCV 2018 · Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze ·

Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large scale datasets. In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network. We apply DeepCluster to the unsupervised training of convolutional neural networks on large datasets like ImageNet and YFCC100M. The resulting model outperforms the current state of the art by a significant margin on all the standard benchmarks.

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Clustering

Deep Clustering

Image Clustering

Semantic Segmentation

Unsupervised Semantic Segmentation

Datasets

CIFAR-10

ImageNet

CIFAR-100

Cityscapes

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ImageNet-S

Results from the Paper

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Ranked #1 on Image Clustering on CIFAR-100 (Train Set metric, using extra training data)

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
Image Clustering	CIFAR-100	DeeperCluster	Accuracy	0.189	# 19	Compare
Image Clustering	CIFAR-100	DeeperCluster	Train Set	Train+Test	# 1	Compare
Unsupervised Semantic Segmentation	Cityscapes test	MDC	mIoU	7.1	# 10	Compare
Unsupervised Semantic Segmentation	Cityscapes test	MDC	Accuracy	40.7	# 10	Compare
Unsupervised Semantic Segmentation	ImageNet-S-50	MDC (Supervised pretrain)	mIoU (val)	14.6	# 5	Compare
Unsupervised Semantic Segmentation	ImageNet-S-50	MDC (Supervised pretrain)	mIoU (test)	14.3	# 5	Compare

Results from Other Papers

Task	Dataset	Model	Metric Name	Metric Value	Rank	Compare
Image Clustering	CIFAR-10	DeepCluster	Accuracy	0.374	# 24	See all
			NMI	-	# 26	See all
			Train set	Train+Test	# 1	See all
			ARI	-	# 26	See all
			Backbone	ResNet-34	# 1	See all

Methods

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DeepCluster • k-Means Clustering

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Deep Clustering for Unsupervised Learning of Visual Features

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