Introduction#

Machine learning models are excellent tools for making predictions or classifications. However, they’re not infallible; occasionally, they may make mistakes. Some of the most enlightening mistakes are the so-called “hard negatives” — instances where the model confidently produces the incorrect output. Understanding and learning from these instances through hard negative mining can significantly improve the model’s performance.

Understanding Hard Negative Mining#

In machine learning, “hard negatives” refer to examples that are challenging for the model to classify correctly. They are the negatives that the model most often misclassifies. Hard negative mining is a strategy for improving the performance of a model by focusing on these difficult-to-classify instances.