Questions tagged [triplet-loss]
Triplet losses are defined in terms of the contrast between three inputs. Each of the inputs has an associated class label, and the goal is to map all inputs of the same class to the same point, while all inputs from other classes are mapped to different points some distance away. It's called a triplet because the loss is computed using an anchor, a sample belonging to the same class as the anchor, and a sample belonging to a different class.
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In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. What could cause this?
I haven't even finished 1 epoch, so I don't think it could any sort of overfitting. I am training on a very large amount of data (27 gb of text) so it'll still be a while before I even reach one epoch....
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Learning useful semantic representations of data
Training a neural network on its final task (e.g. classification) right from the beginning is not always the best way to go. I'd like to make a short list of recognized methods of motivating a NN to ...
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Why does cotrastive loss and triplet loss have the margin element in them?
Trying to learn Siamese networks for ranking tasks from here, I find it hard to understand why triplet loss and contrastive losses have the "margin" element in them.
The contrastive loss
$L(...
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