testing time (*ours* in Table \[ffmethods\]), training it on $50\%$ and $25\%$ resolutions, where it is used for at testing time (*ms res 2+*) and training it only on $100\%$ resolution (*ms res 1*). As can be seen in Table \[ffmethods\] training on all resolutions (*ours*) clearly performs best. Likely, mixed training data performs best as samples of the highest resolution provide the largest entropy while samples of lower resolutions fit better to the problem. However, training samples of lower resolutions seem to harm training for higher resolutions. Therefore, we use an extra CNN for the highest resolution.

Loss Functions and Mining {#s42}
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We compare our loss $l_t$ to other state-of-the-art losses and Hard Mining [@simo2015discriminative] in Figure \[losses\] and Table \[trainkitti\]. As shown in the table, our thresholded loss $l_t$ with $t=0.3$ clearly outperforms all other losses. DrLIM [@hadsell2006dimensionality] reduces the mentioned flaw in the hinge loss, by training samples with small hinge loss less. While this clearly reduces the error compared to hinge, it cannot compete with our thresholded loss $l_t$. Furthermore, no speedup during training is possible like with our approach. CENT. (CENTRIFUGE) [@gadot2015patchbatch] is a variant of DrLIM which performs worse than DrLIM in our 