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@@ -103,7 +103,7 @@ There are couple of problems with siamese and triplet networks:
 2. We generate pairs/triplets *randomly*. As the training continues, more and more pairs/triplets are **easy** to deal with (their loss value is very small or even 0), *preventing the network from training*. We need to provide the network with **hard examples**.
 3. Each image that is fed to the network is used only for computation of contrastive/triplet loss for only one pair/triplet. The computation is somewhat wasted; once the embedding is computed, it could be reused for many pairs/triplets.
 
-To deal with these issues efficiently, we'll feed a network with standard mini-batches as we did for classification. The loss function will be responsible for selection of hard pairs and triplets within mini-batch. If we feed the network with 16 images per 10 classes, we can process up to $159*160/2 = 12720$ pairs and $10*16*15/2*(9*16) = 172800$ triplets, compared to 80 pairs and 53 triplets in previous implementation. 
+To deal with these issues efficiently, we'll feed a network with standard mini-batches as we did for classification. The loss function will be responsible for selection of hard pairs and triplets within mini-batch. If we feed the network with 16 images per 10 classes, we can process up to 159\*160/2 = 12720 pairs and 10\*16\*15/2\*(9\*16) = 172800 triplets, compared to 80 pairs and 53 triplets in previous implementation. 
 
 Usually it's not the best idea to process all possible pairs or triplets within a mini-batch. We can find some strategies on how to select triplets in [2] and [3].