23 October 2021
# Unsupervised Pre-Training of Image Features on Non-Curated Data

by Caron, ..., Joulin (ICCV 2019)

## Background

See the authorâ€™s previous paper
which introduced DeepCluster.

## Research Goal

Use self-supervised training and clustering to do unsupervised learning

## Approach

Deeper Cluster. Rotate images (set of possible rotations: \(Y\)) and optimize K-Means,
where \(Z\) are the cluster assignments

\[\arg \min_{\theta, W} \frac{1}{N}\sum_{n=1}^N l(y_n \otimes z_n, W f_{\theta}(x_n))\]
To make the space \(Z \times Y\) smaller, they use 2-level hierarchy of cluster labels.
Use 2-level hierarchy. To get superclusters, they split the dataset into \(m\) subsets.

## Results

Accuracy of linear classifiers on ImageNet and
Places205 using the activations from different layers as fea-
tures. We compare a VGG-16 trained with supervision on
ImageNet to VGG-16 trained with either RotNet or Deep-
erCluster on YFCC100M. Exact numbers are in Appendix

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