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

International Conference on Computer Vision (ICCV)


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 non-curated 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 [44], achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only non-curated raw 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

Related Publications

All Publications

Federated Learning for User Privacy and Data Confidentiality Workshop At ICML - July 24, 2021

Federated Learning with Buffered Asynchronous Aggregation

John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek, Dzmitry Huba

ISMAR - July 29, 2021

Instant Visual Odometry Initialization for Mobile AR

Alejo Concha, Michael Burri, Jesus Briales, Christian Forster, Luc Oth

UAI - July 28, 2021

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization

Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger

ACM MM - October 20, 2021

EVRNet: Efficient Video Restoration on Edge Devices

Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy