Label Noise, Problems and Solutions

Binay Gupta, Anirban Chatterjee, Kunal Banerjee.

Introduction:

Label noise refers to errors or inaccuracies in the assigned labels or annotations of a dataset. While supervised machine learning algorithms rely on correctly labeled data, real-world scenarios often introduce incorrect labels for various reasons. In this blog, we will explore the sources and challenges associated with label noise, as well as the solution offered by the Progressive Label Correction (PLC) technique. Additionally, we will showcase a project where PLC has significantly improved …

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