Overview
Stochastic Weight Averaging (SWA) inotora avhareji yakapusa yehuremu hwemodhi kubva akati wandei mapoinzi kunonoka mukudzidziswa pane kungochengeta mufananidzo wekupedzisira. Uhu hunyengeri hwakachipa hunowanzo dzika modhi munzvimbo yakapfava, yakafara nzvimbo yekurasika, iyo inowanzo wedzera zvinooneka zvirinani pane isingaonekwe data.
Stochastic Weight Averaging ndeye tekinoroji yekuvaka block inobata mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero.
Deep Dive
Yakaunzwa naIzmailov, Wilson nevamwe vaaishanda navo muna 2018, SWA inoshandisa fungidziro yekuti SGD ine chisingaperi kana cyclical kudzidza mwero haichinji kune imwe nzvimbo - inosvetuka ichitenderedza kumucheto kwemupata wakafara, wakafuratira. Panzvimbo pekutora imwe yeidzo nzvimbo dzekumisa dzine ruzha, SWA inomhanya zvine mwero (kazhinji inogara kana kutenderera) mwero wekudzidza wenguva yekupedzisira uye avhareji huremu hwainoshanyira, kazhinji nguva yega yega. Huremu hweavhareji hunogara padyo nepakati penzvimbo yakati sandara. Nekuda kwekuti batch-normalization statistics inoverengerwa kune chaiwo huremu, SWA inoda imwe yekuwedzera kumberi kupfuura data kudzoreredza BN inomhanya nzira uye misiyano yeavhareji modhi. Mutengo wacho ndewemahara, uye kuwana kwechokwadi kunopindirana mumhando dzemifananidzo uye nekupfuura.
Technical Insight
SWA inochengetedza avhareji inomhanya w_SWA = (n·w_SWA + w_i)/(n+1) yakagadziridza kutenderera kwega kwega, nepo mhenyu yeSGD modhi inoramba ichiongorora nemwero wakakura wekudzidza. Avhareji yehuremu nzvimbo inokwana ensemble munzvimbo yebasa asi inodhura imwe modhi pakunongedza, kwete mazhinji. Iyo yakakosha meshini ndeyekuti flat minima yakasimba kune huremu kukanganisa, saka iyo yekudzidzira / bvunzo yekurasikirwa nzvimbo inogara yakabatana, ichidzikisa gaka rekuita.
Mastering Stochastic Weight Avhareji
Stochastic Weight Averaging (SWA) inotora avhareji yakapusa yehuremu hwemodhi kubva akati wandei mapoinzi kunonoka mukudzidziswa pane kungochengeta mufananidzo wekupedzisira. Uhu hunyengeri hwakachipa hunowanzo dzika modhi munzvimbo yakapfava, yakafara nzvimbo yekurasika, iyo inowanzo wedzera zvinooneka zvirinani pane isingaonekwe data. Stochastic Weight Averaging ndeye tekinoroji yekuvaka block inobata mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero. Kuti uvake kunzwisisa kwakadzama, bata Stochastic Weight Averaging semuenzaniso wekushandisa, kwete chinhu chimwe chete: tsanangura zvinodikanwa, tsanangura fungidziro, uye patsanura izvo zvingaitwe nehurongwa hwakavimbika kubva kune zvichiri kuda kutonga kwenyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa Stochastic Weight Averaging inogonesa zvivakwa, data, uye sarudzo dzezvivakwa zvinopesana nekuvimbika uye mutengo. Ivo vanonyora zvakajeka maitiro ebudiriro, bvunzo vachipokana ne data rechokwadi uye mafambiro ebasa, uye iterate zvichibva pane zvakacherechedzwa maitiro ekutadza kwete kuhwina-nguva imwe chete yebhenji. Apa ndipo apo kunzwisisa kwe theoretical kunoshanduka kuve kugona kwakasimba pane chigadzirwa, mutemo, uye mashandiro.
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore. Panguva imwecheteyo, Kukwirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba. Nzira yakatsiga ndeyekubatanidza kukurumidza kuyedza nekutonga: mhanyisa vatyairi vendege, tora humbowo, buritsa matanda esarudzo, uye urambe uchivandudza chengetedzo semaitiro emuenzaniso, zvinotarisirwa nemushandisi, uye zvinodikanwa zvekutonga.
Strategic Impact
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Real-World Implementation
Kuwedzera bvunzo kurongeka kweResNet uye DenseNet mufananidzo classifiers paCIFAR uye ImageNet pasina imwe yekuwedzera mutengo.
SWAG (SWA-Gaussian) inogadzira fungidziro yekusavimbika yakayerwa yekufembera inotarisisa kubva kune imwechete kudzidziswa kumhanya.
EMA-ye-huremu inodzikamisa iyo sampling network mukuparadzira mifananidzo jenareta seStable Diffusion.
Kugadzira 'soups yemuenzaniso' neaverage yakawanda-yakanyatso gadziridzwa nzvimbo dzekutarisa kuti uvandudze kusimba pasina kudzidziswazve.
Maitiro Ekuita
Stochastic Weight Avhareji mukuita
Kuwedzera bvunzo kurongeka kweResNet uye DenseNet mufananidzo classifiers paCIFAR uye ImageNet pasina imwe yekuwedzera mutengo.
Kuwedzera bvunzo kurongeka kweResNet uye DenseNet mifananidzo yekirasi paCIFAR neImageNet isina imwe mari yekunyepedzera Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura mhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Stochastic Weight Avhareji mukuita
SWAG (SWA-Gaussian) inogadzira fungidziro yekusavimbika yakayerwa yekufembera inotarisisa kubva kune imwechete kudzidziswa kumhanya.
SWAG (SWA-Gaussian) inogadzira fungidziro yekusavimbika yakasarudzika kubva kune imwechete kudzidziswa kumhanya Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Stochastic Weight Avhareji mukuita
EMA-ye-huremu inodzikamisa iyo sampling network mukuparadzira mifananidzo jenareta seStable Diffusion.
EMA-ye-huremu inodzikamisa iyo sampling network mumajenareta emifananidzo seStable Diffusion Matimu anowanzo kuwana mibairo iri nani kana vachitsanangudza zvemhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Stochastic Weight Avhareji mukuita
Kugadzira 'soups yemuenzaniso' neaverage yakawanda-yakanyatso gadziridzwa nzvimbo dzekutarisa kuti uvandudze kusimba pasina kudzidziswazve.
Kugadzira 'soups yemuenzaniso' neaverage yakawanda-yakarongedzwa macheki kuti uvandudze kusimba pasina kudzidzisa Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Njodzi & Guardrails
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.
Implementation Roadmap
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Benchmark pasi pechokwadi mutoro uye data mamiriro.
Benchmark pasi pechokwadi mutoro uye data mamiriro. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.