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Label Noise

What is Label Noise?

Label noise, also known as mislabeling or annotation errors, refers to inaccuracies in labeled datasets used to train Edge AI models. These errors can arise from human mistakes, sensor malfunctions, or ambiguous data, leading to lower model accuracy and unreliable predictions.

Why Is It Used?

Understanding label noise helps Edge AI engineers identify potential pitfalls in training datasets and improve model robustness. Correctly handling label noise ensures that AI applications on devices like IoT sensors or edge cameras produce reliable, real-time insights.

How Is It Used?

  • Detecting mislabeled data during preprocessing.

  • Applying noise-robust algorithms for training Edge AI models.

  • Enhancing model performance by reducing misclassification.

Types of Label Noise

  • Random Noise: Errors occur without a specific pattern.

  • Systematic Noise: Mislabels follow a consistent bias, often from faulty sensors or annotation guidelines.

  • Class-dependent Noise: Certain categories are more prone to mislabeling than others.

Benefits of Label Noise

  • Improves AI model accuracy at the edge.

  • Reduces false predictions in real-time IoT applications.

  • Enhances reliability of decision-making for edge devices.

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