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Semi-Supervised Learning
Basics
Nhungamiro yehunyanzvi
Consistency Regularization muSemi-Yakatariswa Kudzidza
Consistency regularization inodzidzisa modhi kuti ipe mhinduro imwechete kana mapindiro asina kunyorwa akavhiringika munzira diki, dzinochengetedza mavara.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
Inokutendera kuti udzidze kubva kumirwi mikuru yedata isina kunyorwa, kucheka zvinoshamisa kuti mangani akanyorwa nemaoko mienzaniso yaunoda.
Kudzika Kwakadzika
Kunyora data kunodhura; data isina kunyorwa inenge yemahara. Consistency regularization inoshandisa fungidziro yakapfava: kana ukakwenya chinopinza zvishoma (chirimwa, tenderedza, wedzera ruzha, chinjanisa masinonimi) pasina kushandura zvazvinoreva, kufanotaura kwemuenzaniso hakufanirwe kuchinja. Panguva yekudzidziswa iwe unodyisa iwo akafanana asina kunyorwa muenzaniso kuburikidza nenzira mbiri dzakawedzerwa uye wowedzera kurasikirwa kunoranga mutsauko pakati pezviviri zvinobuda. Izvi zvinosundira muganho wesarudzo munzvimbo dzakadzika-density pakati pemasumbu, saka hazvichekere nemapoka akakomba emapoinzi akafanana. Nzira dzakaita sePi-Modhi, Temporal Ensembling, Mean Mudzidzisi, Virtual Adversarial Training, uye FixMatch dzese dzinovaka pane iyi pfungwa, kubatanidza kurasikirwa kudiki kunotariswa pane zvakanyorwa data neiyi isina kudzora kuwirirana kurasikirwa pane yasara.
Kuitwa muNyika yeChokwadi
FixMatch inosvika yakasimba CIFAR-10 kunyatsoita neashoma se4 akanyorwa mifananidzo pakirasi nekumanikidza isina kusimba-kune-simba yekuwedzera kuenderana.
Zvikwata zvekurapa zvekufungidzira zvinodzidzisa matundu classifiers kubva kuzviuru zvema scan asina kunyorwa pamwe chete nemazana mashoma emakesi ane radiologist.
Magadzirirwo ekuzivikanwa kwekutaura ari kunatsiridza pamadimikira kuburikidza nekumanikidza zvinyorwa zvinoenderana pane ruzha-rwakawedzerwa uye zvinomhanyisa-zvinovhiringidza odhiyo.
Kureva kuti Mudzidzisi anodzikamisa kudzidziswa nekuva neavhareji 'mudzidzisi' modhi inoburitsa zvinonangwa zve 'mudzidzi' pamifananidzo isina kunyorwa.
Ramangwana reKusawirirana Regularization muSemi-Yakatariswa Kudzidza
Consistency regularization ikozvino yakajairwa pakuona, kutaura, uye kuwedzera zvinyorwa uye kudzidza kwetabular, uye zvinotsigira akawanda anozvitarisira ega mabikirwo ekugadzirira. Tarisira kubatanidzwa kwakasimba nemafaundesheni modhi, kwainokwenenzvera mataneti akakura akafanodzidziswa uchishandisa ane chitsama chemavara pamwe neakakura asina kunyorwa corpora. Tsvagiridzo iri kudzikisira kunzwisiswa kwayo kune yekuwedzera sarudzo uye zvikumbaridzo zvekuvimbika, uye kuiwedzera kune ine ruzha-chaiyo-yepasirese marongero apo iyo label-kuchengetedza fungidziro dzimwe nguva inoputsika.
Kuzvishandisa
Nei zvichikosha
- Mutengo uye bhajeti. Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
- Sarudzo dzakajeka. Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
- Kudzora kwemhando yepamusoro. Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Chenjera
- Kugadzirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba.
- Infrastructure uye mari yekugadzirisa inowanzotarisirwa pasi.
- Chengetedzo uye kucherechedzwa mapundu anogona kukura sezvo masisitimu anowedzera kuoma.
Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Ramba Uchiongorora
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Zviedze iwe pachako
Take the Consistency Regularization in Semi-Supervised Learning quiz
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Mibvunzo inowanzo bvunzwa
Chii chinonzi Consistency Regularization muSemi-Supervised Learning?
Consistency regularization inodzidzisa modhi kuti ipe mhinduro imwechete kana mapindiro asina kunyorwa akavhiringika munzira diki, dzinochengetedza mavara. Inokutendera kuti udzidze kubva kumirwi mikuru yedata isina kunyorwa, kucheka zvinoshamisa kuti mangani akanyorwa nemaoko mienzaniso yaunoda.
What is the main consistency assumption?
The guide defines consistency around predictions staying stable under small meaning-preserving changes.
How is an unlabeled example used in a basic consistency loss?
The guide describes two augmented paths and a loss on prediction differences.
Which is an example of a label-preserving image perturbation when appropriate?
The guide gives crop and rotation as possible small, meaning-preserving changes.
What does the low-density decision-boundary intuition suggest?
The guide says consistency can push boundaries toward low-density regions.
What is a stop-gradient doing in a teacher-student consistency setup?
The guide describes one branch supplying a target while the other is trained to match it.
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