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Label smoothing replaces hard one-hot targets with a softened target distribution during training.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It can regularize some models, but its effects on accuracy, calibration, and distillation depend on the task, model, and evaluation.
Kudzika Kwakadzika
With K classes, one common label-smoothing convention assigns 1 − ε to the correct class and distributes ε across the remaining classes; implementations may instead distribute ε across all classes, so check the loss definition. This prevents the target from demanding probability one for the labeled class and can discourage extreme logits. The Inception-v3 work and the Transformer paper are prominent documented uses of the method. Research has found calibration and generalization benefits in some settings, while also showing that smoothing can remove information about similarities among incorrect classes and weaken a model as a knowledge-distillation teacher. It is therefore not a universal accuracy or calibration improvement. Choose ε and evaluate calibration, task quality, and downstream distillation on representative validation data.
Kuitwa muNyika yeChokwadi
ImageNet classification: Kutanga-v3 yakashandisa label kutsetseka (epsilon 0.1) kukwidziridza kumusoro-1 kunyatsoita uye kuderedza kuzvivimba zvakanyanya.
Dudziro yemuchina: yepakutanga Transformer yakaisa label kutsvedzerera kwe0.1, ichitengesa kakuvhiringika kune yakakwira BLEU zvibodzwa.
Kuzivikanwa kwekutaura: zvibodzwa zvakatsiga zvinoderedza kusaziva kusavimbika uye kunatsiridza calibration pane ruzha ruzha.
Zvimiro zvekufungidzira zvekurapa: kutsvedzerera kunopa mikana yakarimwa zvirinani, yakakosha kana chibodzwa chekuvimba chichizivisa sarudzo dzekiriniki.
Ramangwana reLabel Smoothing
Label smoothing inoramba iine default mukudzidziswa kwakakura, asi tsvagiridzo iri kuenda kune adaptive uye yakadzidza kutsetseka kunogadzirisa epsilon pamuenzaniso kana kirasi pane kushandisa imwechete yakafuratira kukosha. Calibration-yakatarisana nzira dzakaita sefocal kurasikirwa uye tembiricha kuyera zvinowanzoyerwa kana kusanganiswa nazvo. Sezvo mamodheru achikura uye fungidziro yekusavimbika inovimbika inova yakakosha-kuchengetedza, tarisira kutsvedzerera kuve chishandiso chimwe pakati pevazhinji kugadzira zvibodzwa zvekuvimbika, nekunyatsotarisisa kune inozivikanwa kupokana kwayo ne distillation.
Kuzvishandisa
Nei zvichikosha
- Sarudzo dzakajeka. Inokubatsira kuparadzanisa zvakajeka zvichemo zvehunyanzvi kubva mumutauro wekushambadzira.
- Mutengo uye bhajeti. Iwe unogona kubvunza zvirinani kuita mibvunzo usati washandisa mari kana nguva.
- Team uye workflow. Zvikwata zvine nzwisiso yakagovaniswa inoita zvirinani chigadzirwa, mutemo, uye sarudzo dzekudzidza.
Chenjera
- Zvikwata zvakasiyana zvinogona kushandisa izwi rimwechete zvakasiyana, saka tsanangura nzvimbo nekukurumidza.
- Benchmarks inogona kutaridzika yakasimba nepo chaiyo-yenyika kuita isina kuenzana.
- Kuregeredza mhando yedata uye zvirongwa zvekuongorora zvinowanzogadzira mhedzisiro isina kusimba.
Matanho anotevera
- Tanga netsanangudzo yemutauro wakajeka yemhedzisiro yaunoda.
- Sarudza metric imwe yekubudirira uye imwe yekutadza mamiriro usati waedzwa.
- Mhanya mutyairi mudiki ane data remumiriri, kwete demo rakakwenenzverwa.
- Nyora apo Label Smoothing inobatsira uye uko nzira dzakareruka dziri nani.
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Zviedze iwe pachako
Take the Label Smoothing quiz
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Mibvunzo inowanzo bvunzwa
Chii chinonzi Label Smoothing?
Label smoothing replaces hard one-hot targets with a softened target distribution during training. It can regularize some models, but its effects on accuracy, calibration, and distillation depend on the task, model, and evaluation.
Chii chinoshandura label smoothing pamusoro pezvinangwa zvekudzidzisa?
Panzvimbo peiyo yakaoma 1.0/0.0 chinangwa, label smoothing inopa (1 - epsilon) kukirasi yechokwadi uye inoparadzira epsilon kune mamwe.
What is one intended effect of replacing hard one-hot targets with smoothed targets?
Smoothing softens the target distribution so the training objective does not demand a one-hot probability vector.
Which landmark architectures are prominent examples of label-smoothing use?
Inception-v3 and the Transformer paper are prominent documented uses of label smoothing; this wording makes no claim that either originated the technique.
What can label smoothing do to calibration?
Research reports calibration effects that depend on the model and evaluation setting; label smoothing is not a universal calibration guarantee.
Chii chinonzi downside yelabel smoothing?
Nekuomesa masumbu uye kudzikamisa hukama hwakapfava pakati pemakirasi, kutsetseka kunogona kubvisa ruzivo rwunotsamira paruzivo.
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