# Deep Clustering for Unsupervised Learning of Visual Features

**Authors:** Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze  
**arXiv:** [1807.05520](https://arxiv.org/abs/1807.05520)  
**Date:** 2018-07-15  
**Summary page:** https://www.summarizepaper.com/en/paper/1807.05520v2/

## Key points

- The paper explores the application of clustering methods in computer vision for training visual features on large-scale datasets.
- The authors propose a method called DeepCluster that learns both neural network parameters and cluster assignments simultaneously.
- DeepCluster utilizes k-means clustering algorithm to group features iteratively and uses the obtained assignments as supervision to update network weights.
- The effectiveness of DeepCluster is evaluated on unsupervised training of convolutional neural networks on datasets like ImageNet and YFCC100M.
- DeepCluster shows significant improvement over current state-of-the-art approaches in feature extraction and representation learning on large-scale datasets.

## Summary

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The paper "Deep Clustering for Unsupervised Learning of Visual Features" by Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze explores the application of clustering methods in computer vision for the end-to-end training of visual features on large-scale datasets. The authors propose a novel method called DeepCluster that simultaneously learns the parameters of a neural network and the cluster assignments of resulting features. This approach utilizes k-means clustering algorithm to group features iteratively and uses the obtained assignments as supervision to update network weights. The effectiveness of DeepCluster is evaluated on unsupervised training of convolutional neural networks on datasets like ImageNet and YFCC100M, showing significant improvement over current state-of-the-art approaches. Overall, this paper presents an innovative combination of clustering and deep learning techniques that shows promise in improving feature extraction and representation learning on large-scale datasets.