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As the adoption of deep learning techniques in industrial applications growswith increasing speed and scale, successful deployment of deep learning modelsoften hinges on the availability, volume, and quality of annotated data. Inthis paper, we tackle the problems of efficient data labeling and annotationverification under the human-in-the-loop setting. We showcase that the latestadvancements in the field of self-supervised visual representation learning canlead to tools and methods that benefit the curation and engineering of naturalimage datasets, reducing annotation cost and increasing annotation quality. Wepropose a unifying framework by leveraging self-supervised semi-supervisedlearning and use it to construct workflows for data labeling and annotationverification tasks. We demonstrate the effectiveness of our workflows overexisting 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 onCIFAR100 with 10% annotated data. When learning with 50% of wrong labels, ourmethod achieves 97.4% Top-1 Accuracy on CIFAR10 and 85.5% Top-1 Accuracy onCIFAR100.

Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation