86:
161:
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.
360:
36:
178:
651:
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
160:
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
668:(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.
104:
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
355:{\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)}
646:
112:
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
108:
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
514:
530:
54:
494:
131:
105:
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.
474:
450:
430:
406:
382:
707:
109:
790:
999:
994:
690:
In
Natural Language Processing, triplet loss is one of the loss functions considered for BERT fine-tuning in the SBERT architecture.
665:
72:
916:
671:
Additionally, triplet loss has been extended to simultaneously maintain a series of distance orders by optimizing a continuous
894:
Hermans, Alexander; Beyer, Lucas; Leibe, Bastian (2017-03-22). "In
Defense of the Triplet Loss for Person Re-Identification".
520:
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
815:
763:
Schroff, F.; Kalenichenko, D.; Philbin, J. (June 2015). "FaceNet: A unified embedding for face recognition and clustering".
521:
967:
Reimers, Nils; Gurevych, Iryna (2019-08-27). "Sentence-BERT: Sentence
Embeddings using Siamese BERT-Networks".
89:
Effect of triplet loss minimization in training: the positive is moved closer to the anchor than the negative.
85:
702:
664:
tasks such as re-identification, a prevailing belief has been that the triplet loss is inferior to using
867:
499:
841:
855:
717:
138:
641:{\displaystyle {\mathcal {J}}=\sum _{i=1}^{{}M}{\mathcal {L}}\left(A^{(i)},P^{(i)},N^{(i)}\right)}
968:
949:
895:
845:
796:
768:
169:
738:
683:, which has been demonstrated to offer performance enhancements of visual-semantic embedding in
941:
786:
479:
116:
931:
778:
712:
684:
142:
101:
880:
693:
Other extensions involve specifying multiple negatives (multiple negatives ranking loss).
661:
154:
915:
Zhou, Mo; Niu, Zhenxing; Wang, Le; Gao, Zhanning; Zhang, Qilin; Hua, Gang (2020-04-03).
859:
459:
435:
415:
391:
367:
150:
146:
988:
953:
800:
165:
97:
17:
782:
134:
945:
936:
765:
2015 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR)
973:
900:
773:
850:
84:
739:"Large Scale Online Learning of Image Similarity Through Ranking"
29:
924:
Proceedings of the AAAI Conference on
Artificial Intelligence
652:
the network, and the outputs are used in the loss function.
569:
536:
184:
737:
Chechik, G.; Sharma, V.; Shalit, U.; Bengio, S. (2010).
50:
816:"Learning a distance metric from relative comparisons"
533:
502:
496:
is a margin between positive and negative pairs, and
482:
462:
438:
418:
394:
370:
181:
119:
917:"Ladder Loss for Coherent Visual-Semantic Embedding"
45:
may be too technical for most readers to understand
640:
508:
488:
468:
444:
424:
400:
376:
354:
125:
823:Advances in Neural Information Processing Systems
141:for the purpose of learning embeddings, such as
679:) of distance inequalities. This leads to the
8:
842:"Deep metric learning using Triplet network"
325:
285:
270:
230:
708:t-distributed stochastic neighbor embedding
972:
935:
899:
849:
772:
621:
602:
583:
568:
567:
560:
559:
548:
535:
534:
532:
501:
481:
461:
437:
417:
393:
369:
329:
284:
274:
229:
183:
182:
180:
137:-style formulation. It is often used for
118:
73:Learn how and when to remove this message
57:, without removing the technical details.
27:Function for machine learning algorithms
840:Ailon, Nir; Hoffer, Elad (2014-12-20).
729:
876:
865:
55:make it understandable to non-experts
7:
746:Journal of Machine Learning Research
814:Schultz, M.; Joachims, T. (2004).
509:{\displaystyle \operatorname {f} }
503:
308:
288:
253:
233:
25:
168:can be described by means of the
34:
628:
622:
609:
603:
590:
584:
1:
1000:Machine learning algorithms
1016:
995:Artificial neural networks
456:of a different class from
783:10.1109/CVPR.2015.7298682
656:Comparison and Extensions
937:10.1609/aaai.v34i07.7006
489:{\displaystyle \alpha }
126:{\displaystyle \alpha }
875:Cite journal requires
703:Siamese neural network
642:
566:
510:
490:
470:
446:
426:
402:
378:
356:
127:
90:
643:
544:
511:
491:
471:
447:
427:
412:of the same class as
403:
379:
357:
128:
88:
767:. pp. 815–823.
675:with a chain (i.e.,
531:
500:
480:
460:
436:
416:
392:
368:
179:
117:
860:2014arXiv1412.6622H
718:Similarity learning
139:learning similarity
930:(7): 13050–13057.
638:
506:
486:
466:
442:
422:
398:
374:
352:
170:Euclidean distance
123:
91:
792:978-1-4673-6964-0
469:{\displaystyle A}
445:{\displaystyle N}
425:{\displaystyle A}
401:{\displaystyle P}
377:{\displaystyle A}
83:
82:
75:
16:(Redirected from
1007:
979:
978:
976:
964:
958:
957:
939:
921:
912:
906:
905:
903:
891:
885:
884:
878:
873:
871:
863:
853:
837:
831:
830:
820:
811:
805:
804:
776:
760:
754:
753:
743:
734:
713:Learning to rank
685:learning to rank
673:relevance degree
666:surrogate losses
647:
645:
644:
639:
637:
633:
632:
631:
613:
612:
594:
593:
573:
572:
565:
561:
558:
540:
539:
516:is an embedding.
515:
513:
512:
507:
495:
493:
492:
487:
475:
473:
472:
467:
451:
449:
448:
443:
431:
429:
428:
423:
407:
405:
404:
399:
383:
381:
380:
375:
361:
359:
358:
353:
351:
347:
334:
333:
328:
324:
304:
279:
278:
273:
269:
249:
213:
209:
188:
187:
143:learning to rank
132:
130:
129:
124:
102:machine learning
78:
71:
67:
64:
58:
38:
37:
30:
21:
18:Contrastive loss
1015:
1014:
1010:
1009:
1008:
1006:
1005:
1004:
985:
984:
983:
982:
966:
965:
961:
919:
914:
913:
909:
893:
892:
888:
874:
864:
839:
838:
834:
818:
813:
812:
808:
793:
762:
761:
757:
741:
736:
735:
731:
726:
699:
662:computer vision
658:
617:
598:
579:
578:
574:
529:
528:
498:
497:
478:
477:
458:
457:
434:
433:
414:
413:
390:
389:
366:
365:
314:
294:
283:
259:
239:
228:
227:
223:
193:
189:
177:
176:
155:metric learning
151:thought vectors
147:word embeddings
115:
114:
79:
68:
62:
59:
51:help improve it
48:
39:
35:
28:
23:
22:
15:
12:
11:
5:
1013:
1011:
1003:
1002:
997:
987:
986:
981:
980:
959:
907:
886:
877:|journal=
832:
806:
791:
755:
728:
727:
725:
722:
721:
720:
715:
710:
705:
698:
695:
657:
654:
649:
648:
636:
630:
627:
624:
620:
616:
611:
608:
605:
601:
597:
592:
589:
586:
582:
577:
571:
564:
557:
554:
551:
547:
543:
538:
518:
517:
505:
485:
465:
454:negative input
441:
421:
410:positive input
397:
373:
362:
350:
346:
343:
340:
337:
332:
327:
323:
320:
317:
313:
310:
307:
303:
300:
297:
293:
290:
287:
282:
277:
272:
268:
265:
262:
258:
255:
252:
248:
245:
242:
238:
235:
232:
226:
222:
219:
216:
212:
208:
205:
202:
199:
196:
192:
186:
122:
81:
80:
42:
40:
33:
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
1012:
1001:
998:
996:
993:
992:
990:
975:
970:
963:
960:
955:
951:
947:
943:
938:
933:
929:
925:
918:
911:
908:
902:
897:
890:
887:
882:
869:
861:
857:
852:
847:
843:
836:
833:
828:
824:
817:
810:
807:
802:
798:
794:
788:
784:
780:
775:
770:
766:
759:
756:
751:
747:
740:
733:
730:
723:
719:
716:
714:
711:
709:
706:
704:
701:
700:
696:
694:
691:
688:
686:
682:
678:
674:
669:
667:
663:
655:
653:
634:
625:
618:
614:
606:
599:
595:
587:
580:
575:
562:
555:
552:
549:
545:
541:
527:
526:
525:
523:
483:
463:
455:
439:
419:
411:
395:
387:
371:
363:
348:
344:
341:
338:
335:
330:
321:
318:
315:
311:
305:
301:
298:
295:
291:
280:
275:
266:
263:
260:
256:
250:
246:
243:
240:
236:
224:
220:
217:
214:
210:
206:
203:
200:
197:
194:
190:
175:
174:
173:
171:
167:
166:loss function
162:
158:
156:
152:
148:
144:
140:
136:
120:
111:
106:
103:
99:
98:loss function
95:
87:
77:
74:
66:
56:
52:
46:
43:This article
41:
32:
31:
19:
962:
927:
923:
910:
889:
868:cite journal
835:
826:
822:
809:
764:
758:
752:: 1109–1135.
749:
745:
732:
692:
689:
680:
676:
672:
670:
659:
650:
522:optimization
519:
453:
409:
386:anchor input
385:
163:
159:
107:
94:Triplet loss
93:
92:
69:
60:
44:
681:Ladder Loss
989:Categories
974:1908.10084
901:1703.07737
774:1503.03832
724:References
135:hinge loss
63:April 2019
954:208139521
946:2374-3468
851:1412.6622
801:206592766
546:∑
484:α
339:α
326:‖
312:
306:−
292:
286:‖
281:−
271:‖
257:
251:−
237:
231:‖
221:
172:function
121:α
829:: 41–48.
697:See also
524:problem
856:Bibcode
687:tasks.
133:in its
49:Please
952:
944:
799:
789:
677:ladder
384:is an
364:where
153:, and
969:arXiv
950:S2CID
920:(PDF)
896:arXiv
846:arXiv
819:(PDF)
797:S2CID
769:arXiv
742:(PDF)
408:is a
110:t-SNE
96:is a
942:ISSN
881:help
787:ISBN
452:is a
164:The
100:for
932:doi
779:doi
660:In
218:max
53:to
991::
948:.
940:.
928:34
926:.
922:.
872::
870:}}
866:{{
854:.
844:.
827:16
825:.
821:.
795:.
785:.
777:.
750:11
748:.
744:.
476:,
432:,
388:,
157:.
149:,
145:,
977:.
971::
956:.
934::
904:.
898::
883:)
879:(
862:.
858::
848::
803:.
781::
771::
635:)
629:)
626:i
623:(
619:N
615:,
610:)
607:i
604:(
600:P
596:,
591:)
588:i
585:(
581:A
576:(
570:L
563:M
556:1
553:=
550:i
542:=
537:J
504:f
464:A
440:N
420:A
396:P
372:A
349:)
345:0
342:,
336:+
331:2
322:)
319:N
316:(
309:f
302:)
299:A
296:(
289:f
276:2
267:)
264:P
261:(
254:f
247:)
244:A
241:(
234:f
225:(
215:=
211:)
207:N
204:,
201:P
198:,
195:A
191:(
185:L
76:)
70:(
65:)
61:(
47:.
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
