What is proxy labels?
Labels or annotations used as substitutes for true labels in machine learning models
proxy labels explained in plain English
Proxy labels are used when the true labels for a dataset are difficult or expensive to obtain. They can be used to train machine learning models, but may not be as accurate as using the true labels.
Analogy
Think of proxy labels like using a map to navigate a new city instead of having a personal guide. The map can get you close to your destination, but it may not show you the exact best route.
Example
For example, in a medical diagnosis application, proxy labels might be used to train a model to diagnose diseases based on symptoms, rather than using actual diagnoses from doctors.
How is proxy labels used?
Proxy labels are used in machine learning to train models when true labels are not available. They can be used in a variety of applications, including image classification, natural language processing, and recommender systems.
Common misconceptions about proxy labels
History
The use of proxy labels has been around for several decades, but has become more popular in recent years with the rise of machine learning and deep learning.
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