What is Self-Agreement and How Can it Improve Your Training Data?

What is Self-Agreement and How Can it Improve Your Training Data?

In this article, we will explain what self-agreement is and introduce an ML study where self-agreement checks were crucial to the quality of the team training data and the accuracy of their model.

For example, a simple inter-annotator agreement workflow would be to send two separate annotators the same piece of data. Then, you would check to see if their annotations are the same. If they are not the same, you could then bring that piece of data to a supervisor to make a ruling on which annotation is correct.

On the other hand, with self-agreement protocols, you would send the same annotator the same piece of data twice to see if they provide the same label both times. For example, if they are tasked with annotating 100 images, you could set image 1 and image 35 as the same image, evaluate the result, and repeat this process many times. Theoretically, you could send an annotator the same data more than twice, but the effect is minimized because the annotator starts to realize that they’ve seen this data point before.

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Improve Your AI Training Data Using Self-Agreement Protocols

In this article, we will explain what self-agreement is and introduce an ML study where self-agreement checks were crucial to the quality of the team training data and the accuracy of their model.

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