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Pseudo-Labeling uye Kuzvidzidzisa
Pseudo-labeling inzira inotariswa semi-inotariswa apo modhi yakadzidziswa pane diki yakanyorwa seti inoburitsa iwo mavara e data isina kunyorwa, yozodzidzira pane izvo kufanotaura.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It is a simple, powerful way to exploit abundant unlabeled data.
Kudzika Kwakadzika
Kuzvidzidzisa ndeimwe yekare semi-inotariswa mazano. Unotanga wadzidzisa modhi yemudzidzisi pane yakaganhurwa data yakanyorwa. Mudzidzisi anobva afanotaura ma labels kune dziva guru remienzaniso isina kunyorwa; kufembera kwekutenda kwepamusoro kunova pseudo-labels. Muenzaniso wemudzidzi unodzidziswa pamubatanidzwa wemapepa echokwadi uye manyepo, kazhinji kukunda mudzidzisi. Kuvimbo zvikumbaridzo zvine basa: kufanotaura chete pamusoro pekugona kuchekwa kunochengetwa, saka modhi haina kushatiswa nefungidziro yayo isina chokwadi. Misiyano yemazuva ano inosanganisa pseudo-labeling nekuenderana kurongeka. FixMatch, semuenzaniso, inogadzira pseudo-label kubva kune isina kusimba yakawedzerwa mufananidzo uye inodzidzisa iyo modhi kuti ienderane nayo pane yakasimba augmented vhezheni, asi chete kana fungidziro isina simba ichivimba. Noisy Mudzidzi akayera zano paImageNet nekuita kuti mudzidzi akure uye nekuwedzera ruzha (kudonha, kuwedzera) panguva yekudzidziswa kwayo.
Kuitwa muNyika yeChokwadi
Kudzidzira matauriro ehurongwa nekunyora zviuru zvemaawa zveaudio isina kunyorwa nemhando yembeu, wozodzidzirazve pane zvinyorwa zvine chivimbo.
Google's Noisy Student inonatsiridza ImageNet nemazvo nekunyora kakawanda mifananidzo isina kunyorwa ine mudzidzisi uye kudzidzisa mudzidzi mukuru, ane ruzha.
Kunyora dziva guru rekuongorora kwezvokurapa kusina kunyorwa nemuenzaniso wakadzidziswa pamazana mashoma emakesi akanyorwa nenyanzvi kuwedzera seti yekudzidziswa.
Bootstrapping a text classifier for niche domain by pseudo-labeling mamirioni ezvinyorwa zvisina kunyorwa pamusoro pechivimbo chekuvimba.
Ramangwana rePseudo-Kunyora uye Kuzvidzidzisa
Pseudo-labeling inoramba iri musimboti wekudzidza-kunyatsoshanda uye kuwedzera kune-mahombe-modhi yekudzidzisa mapaipi, uko mamodheru akasimba anoburitsa mavara ekugadzira kana kunyange data rekugadzira kudzidzisa madiki kana matsva mamodheru, chimiro che distillation. Tarisira kubatanidzwa kwakasimba nekudzidza kunoshanda (kusarudza kuti ndeipi mienzaniso iyo vanhu vanofanira kuisa), fungidziro iri nani yekusefa pseudo-labels, uye kuenderera mberi kushandiswa mukuzivikanwa kwekutaura, kufungidzira kwekurapa, uye chero dhomeini iyo isina kunyorwa data inodarika nhamba yakanyorwa.
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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Take the Pseudo-Labeling and Self-Training quiz
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Mibvunzo inowanzo bvunzwa
What is Pseudo-Labeling and Self-Training?
Pseudo-labeling inzira inotariswa semi-inotariswa apo modhi yakadzidziswa pane diki yakanyorwa seti inoburitsa iwo mavara e data isina kunyorwa, yozodzidzira pane izvo kufanotaura. Iyo iri nyore, ine simba nzira yekushandisa yakawanda isina kunyorwa data.
How does a basic self-training loop use unlabeled examples?
The guide describes generating pseudo-labels and training with labeled and selected unlabeled examples.
Why apply a confidence threshold to pseudo-labels?
The guide says high-confidence predictions are retained to reduce contamination from uncertain guesses.
What is confirmation bias in pseudo-labeling?
The guide warns that a model’s errors may be reinforced by its generated labels.
In FixMatch, where does the pseudo-label come from?
FixMatch uses a confident prediction from a weak augmentation to train on a strong augmentation.
What is the student-noise idea in Noisy Student?
The guide describes adding noise during student training to encourage generalization.
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