Senior Software Research Engineer Samsung Display America Lab San Jose, California
Despite the immense potential, deep learning-based visual defect detection is often impaired by the inadequate quality of real-world data. Here, we leverage Confident Learning and One-class Classification to tackle two major data issues: 1) mislabeling and 2) limited defects for model development, achieving ?0.85 AUROC on intentionally polluted industrial data.
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