Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features

By Mathilde Caron, Piotr Bojanowski, Julien Mairal, Armand Joulin. 2019

9 citations recorded by the source corpus.

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 74.6% top-1 accuracy on the validation set of ImageNet classification, which is an improvement of +0.7% over the same network trained from scratch.

Subfields: Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

Efficient Visual Pretraining with Contrastive Detection (2021, background): “While the benefits of instance-discrimination approaches have mostly been limited to pretraining from simple datasets such as ImageNet, clustering-based pretraining has proven very successful in leveraging large amounts of uncurated images for transfer learning [3, 6, 7, 20, 31].”
When Does Self-supervision Improve Few-shot Learning? (2019, background): “Other tasks include predicting rotation (Gi-daris et al., 2018), noise (Bojanowski & Joulin, 2017), clusters (Caron et al., 2018; 2019; Wu et al., 2018), number of objects (Noroozi et al., 2017), missing patches (Pathak et al., 2016; Trinh et al., 2019), motion segmentation labels (Pathak et al.,…”