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Consistent Auxiliary Data Source
An auxiliary data source is consistent with the target task when the same input-to-label mapping works across both the auxiliary and target sources. In the cat-image example, the label indicating whether a cat is present can be predicted from the image even without knowing whether the image came from the internet or a mobile app, so internet-image recognition is consistent with mobile-app-image recognition.
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What is the defining condition for an auxiliary data source to be 'consistent' with a target task?
In ML Yearning's cat example, a model must know whether an image came from the internet or a mobile app to correctly label it.
An auxiliary data source is consistent with the target task when the same _____ works across both sources.
Match each term to its role in the concept of a consistent auxiliary data source.
Order the steps for determining whether an auxiliary data source is consistent with a target task.
According to ML Yearning, what is the main practical downside of including a consistent auxiliary data source in training?
ML Yearning states that including a consistent auxiliary source offers 'little downside' and 'some possible significant upside.'
In ML Yearning's cat example, internet images are a consistent auxiliary source because f(x) predicts the label without knowing the image _____.
Match each ML Yearning concept to its correct description in the context of consistent auxiliary data sources.
Order the reasoning steps that justify including a consistent auxiliary data source in model training.
Analyzing Consistency and Cost of Auxiliary Data in ML
Evaluating Cat Recognition Consistency Across Web and App Sources
Role of Image Origin in Consistent Data Mapping