Ebou A Sowe* and Yunusa A Bah
This brief report presents a novel unsupervised learning representation learning method called momentum contrast. Momentum
contrast uses a contrastive learning technique to learn representations by comparing features of related yet dissimilar images
for efficient feature extraction and unsupervised representation learning. Similar images are grouped together, and dissimilar
images are placed far apart. The method builds upon previous works in contrastive learning but includes a momentum
optimisation step to improve representation learning performance and generate better quality representations. Experiments
on various datasets demonstrate that momentum contrast is able to learn high-quality representations, allowing us to directly
use them to achieve competitive performance with fewer labelled examples.