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

Understand

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.