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learning problem instead of a classification problem. Here the network is trained (using a contrastive loss) to output a distance which is small if the image belongs to a known person and large if the image belongs to an unknown person. However, if we want to output the closest images to a given image, we want to learn a ranking and not just a similarity. A triplet loss is used in this case.
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The indices are for individual input vectors given as a triplet. The triplet is formed by drawing an anchor input, a positive input that describes the same entity as the anchor entity, and a negative input that does not describe the same entity as the anchor entity. These inputs are then run through
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Consider the task of training a neural network to recognize faces (e.g. for admission to a high security zone). A classifier trained to classify an instance would have to be retrained every time a new person is added to the face database. This can be avoided by posing the problem as a similarity
657:(i.e., typical classification losses) followed by separate metric learning steps. Recent work showed that for models trained from scratch, as well as pretrained models, a special version of triplet loss doing end-to-end deep metric learning outperforms most other published methods as of 2017.
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algorithms where a reference input (called anchor) is compared to a matching input (called positive) and a non-matching input (called negative). The distance from the anchor to the positive is minimized, and the distance from the anchor to the negative input is maximized. An early formulation
344:{\displaystyle {\mathcal {L}}\left(A,P,N\right)=\operatorname {max} \left({\|\operatorname {f} \left(A\right)-\operatorname {f} \left(P\right)\|}_{2}-{\|\operatorname {f} \left(A\right)-\operatorname {f} \left(N\right)\|}_{2}+\alpha ,0\right)}
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which preserves embedding orders via probability distributions, triplet loss works directly on embedded distances. Therefore, in its common implementation, it needs soft margin treatment with a slack variable
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By enforcing the order of distances, triplet loss models embed in the way that a pair of samples with same labels are smaller in distance than those with different labels. Unlike
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equivalent to triplet loss was introduced (without the idea of using anchors) for metric learning from relative comparisons by M. Schultze and T. Joachims in 2003.
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Natural Language Processing, triplet loss is one of the loss functions considered for BERT fine-tuning in the SBERT architecture.
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Additionally, triplet loss has been extended to simultaneously maintain a series of distance orders by optimizing a continuous
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Hermans, Alexander; Beyer, Lucas; Leibe, Bastian (2017-03-22). "In
Defense of the Triplet Loss for Person Re-Identification".
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This can then be used in a cost function, that is the sum of all losses, which can then be used for minimization of the posed
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Schroff, F.; Kalenichenko, D.; Philbin, J. (June 2015). "FaceNet: A unified embedding for face recognition and clustering".
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Reimers, Nils; Gurevych, Iryna (2019-08-27). "Sentence-BERT: Sentence
Embeddings using Siamese BERT-Networks".
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Effect of triplet loss minimization in training: the positive is moved closer to the anchor than the negative.
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tasks such as re-identification, a prevailing belief has been that the triplet loss is inferior to using
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Other extensions involve specifying multiple negatives (multiple negatives ranking loss).
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Zhou, Mo; Niu, Zhenxing; Wang, Le; Gao, Zhanning; Zhang, Qilin; Hua, Gang (2020-04-03).
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Proceedings of the AAAI Conference on
Artificial Intelligence
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the network, and the outputs are used in the loss function.
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Chechik, G.; Sharma, V.; Shalit, U.; Bengio, S. (2010).
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is a margin between positive and negative pairs, and
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