What is the meaning of "easy negatives" in the context of machine learning? 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? 

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OK, I think I understood what this means. Hard and easy negatives are ones which have a relatively large and small values for the loss function respectively.