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What does the term "easy negatives" exactly mean in the context of machine learning for a classification problem or any problem in general?
From a quick google search, I think it means just negative examples in the training set.
Can someone please elaborate a bit more on why the term "easy" is brought into the picture?
Below, there is a screenshot taken from the paper where I found this term, which is underlined.
OK, I think I understood what this means.
Hard and easy negatives are the ones that have relatively large and small values for the loss function, respectively.
