Category: Data Labeling
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Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance
Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance A few labels go a long way in anomaly detection The post Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance appeared first on Towards Data Science. Shuai Guo Go to original source
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How to Measure Real Model Accuracy When Labels Are Noisy
How to Measure Real Model Accuracy When Labels Are Noisy Ground truth is never perfect. From scientific measurements to human annotations used to train deep learning models, ground truth always has some amount of errors. ImageNet, arguably the most well-curated image dataset has 0.3% errors in human annotations. Then, how can we evaluate predictive models…