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

## TL;DR

This paper introduces a new unsupervised pre-training method for image features using large-scale uncurated data, achieving state-of-the-art results and improving supervised classification accuracy.

## Contribution

The paper presents a novel self-supervised clustering approach that effectively leverages massive uncurated datasets for visual feature learning.

## Key findings

- Achieved state-of-the-art results on standard benchmarks for unsupervised methods.
- Pre-training with our method improves supervised ImageNet classification accuracy.
- Validated the effectiveness of unsupervised learning on 96 million uncurated images.

## Abstract

Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster.