  As the adoption of deep learning techniques in industrial applications grows
with increasing speed and scale, successful deployment of deep learning models
often hinges on the availability, volume, and quality of annotated data. In
this paper, we tackle the problems of efficient data labeling and annotation
verification under the human-in-the-loop setting. We showcase that the latest
advancements in the field of self-supervised visual representation learning can
lead to tools and methods that benefit the curation and engineering of natural
image datasets, reducing annotation cost and increasing annotation quality. We
propose a unifying framework by leveraging self-supervised semi-supervised
learning and use it to construct workflows for data labeling and annotation
verification tasks. We demonstrate the effectiveness of our workflows over
existing methodologies. On active learning task, our method achieves 97.0%
Top-1 Accuracy on CIFAR10 with 0.1% annotated data, and 83.9% Top-1 Accuracy on
CIFAR100 with 10% annotated data. When learning with 50% of wrong labels, our
method achieves 97.4% Top-1 Accuracy on CIFAR10 and 85.5% Top-1 Accuracy on
CIFAR100.
