What does "Weakly Labeled Data" mean?

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Weakly labeled data refers to information used in training machines that does not have clear or detailed labels. Unlike fully labeled data, where each piece has exact information about what it shows, weakly labeled data might only have a general idea or a few important details about the content.

This type of data is useful when gathering detailed labels is difficult, costly, or time-consuming. For example, in medical images, a doctor might mark only the most important areas instead of labeling every small detail.

Machines can learn from this kind of data by finding patterns and making educated guesses. Even though the labels may not be perfect, they can still help the machine improve its performance over time. This approach is valuable in fields like robotics and medicine, where precise labeling can be hard to achieve.