The conference paper Jean Ogier Du Terrail, Frédéric Jurie. ON THE USE OF DEEP NEURAL NETWORKS FOR THE DETECTION OF SMALL VEHICLES IN ORTHO-IMAGES. IEEE International Conference on Image Processing, Sep 2017, Beijing, China. (PDF) uses the terms "hard-mining" (6×), "hard mining" (2×), "hard examples" (3×), "hard example mining" (1×), "hard negative" (2×), "hard-negative samples" (1×) and "hard-negative-mining strategies" (1×).
I have no idea what the "hard" specifyer means in this context. As it is mentioned in conjunction with bootstrapping, I suspect that it might be a term from statistics rather than GIS or AI/IR/machine learning/visual object detection or (deep convolutional) artificial neural networks. (It might, of course, be a remote-sensing-specific term.)
2.3. Hard-Mining strategies
Bootstrapping offers a lot of liberties on how the hard examples are chosen. One could for instance pick a limited number of false positives per image or one could fix a threshold and only pick a false positive if its score is superior to a fixed threshold (0.5 for instance). [...]
Does "hard" (in general, or within the terms listed above) mean anything specific in statistics, and if so, what? From the context, I don't suppose that it refers to the difficulty of the problem.
I figured it may be related "hard evidence", but that didn't help me in determining what it might mean here.