negative of a different class and you try to push those apart while pulling those together the problem here they say is the problem of hard negative sampling in order for this to make sense you need the negative sample to be what's called a hard negative sample so this the call is hard negative mining because you only have one negative sample you better make this something where the network can learn from right and if it's too easy the network can't learn anything and they're more thereby you have the problem of hard negative mining where