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Deep Clustering for Unsupervised Learning of Visual Features

arXiv 1807.05520 ↗First posted 15 Jul 2018cs.CVcited by 1,020

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Abstract (arXiv)

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.

Key facts. Cited by 1,020 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2018.

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Citing papers per year: 2021: 172; 2022: 198; 2023: 257; 2024: 169; 2025: 152; 2026: 72.

1,020 papers.

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  12. 2024The Faiss librarycited by 370
38 more
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  11. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
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  14. 2021BEVT: BERT Pretraining of Video Transformerscited by 169
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  35. 2021State Entropy Maximization with Random Encoders for Efficient Explorationcited by 107
  36. 2021SPICE: Semantic Pseudo-labeling for Image Clusteringcited by 104
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  38. 2021Pre-training without Natural Imagescited by 100

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