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Distance Metrics in Machine Learning
Basics
Basics GUIDE
Kurasika katatu kunodzidzisa neural network kuisa zvinhu zvakafanana padyo pamwe chete uye zvinhu zvakasiyana zviri kure munzvimbo yekumisikidza.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Ndiwo hwaro kuseri kwekuzivikanwa kwechiso, kutsvaga mifananidzo, uye masisitimu ekurudziro anoda kuenzanisa zvinhu pane kungozvironga.
Metric yekudzidza inodzidzisa modhi kugadzira embeddings, mavekita uko kureba kunoratidza kufanana. Triplet kurasikirwa kunoita izvi uchishandisa matatu ekuisa panguva: anchor, yakanaka (yakafanana kirasi seiyo anchor), uye isina kunaka (yakasiyana kirasi). Chinangwa chinosundidzira chibatiso pedyo nechakanaka pane chakashata neinenge yakatemwa muganho. Pakare, kurasikirwa kuri max(0, d(a,p) - d(a,n) + margin), uko d inowanzonzi Euclidean chinhambwe. Google's 2015 FaceNet yakasimudzira nzira iyi, kudzidza 128-dimensional yekumisikidza kumeso zvakananga. Kana uchinge wadzidziswa, unoenzanisa chero zvinhu zviviri nekombuta chinhambwe, hapana kudzidziswazve kunodiwa kune zvitsva kuzivikanwa. Uku kugona kwakavhurika-seti ndiko kusaka metric yekudzidza masimba ekuongorora uye kudzoreredza mabasa emhando isingagone kubata zviri nyore.
Kuongororwa kwechiso cheFaceNet: nhare uye magedhi epasipoti anosimbisa chitupa nekutarisa kana maviri ekumisikidza kumeso achiwira mukati mechikumbaridzo.
Kutsvaga kwechigadzirwa chinooneka: e-commerce saiti rega vatengi vaise pikicha uye vatore zvinooneka zvinhu zvakafanana nepedyo-yepedyo-embedding yekutarisa.
Ongororo yeMutauriri: vabatsiri vezwi vanoisa sampuli yezwi uye voienzanisa neprofile yakanyoreswa kuratidza kuti ndiani ari kutaura.
Siginicha nerunyoro rwekusimbisa: mabhangi anomisikidza referensi uye masiginecha emibvunzo uye mureza wefogeries kana chinhambwe chadarika chikamu chakadzidziswa.
Kurasika kwakachena katatu kuri kuramba kuchitsiviwa nezvinangwa zvebatch-wide senge-multi-kufanana, proxy-anchor, uye kurasikirwa kwakasiyana (InfoNCE) inofananidza mapairi mazhinji padanho uye kuchinjika nekukurumidza. Nzira dzekuzvitarisira dzakaita seSimCLR dzinoratidza metric kudzidza inogona kushanda isina mavara nekubata akawedzera maonero seakanaka. Sezvo vector dhatabhesi uye kudzoreredza-yakawedzera chizvarwa kuwedzera, yakadzidzwa embeddings inotsigisa semantic yekutsvaga pabhiriyoni-chinhu chikero, saka pfungwa yepakati-sekufanana-kufanana iri kuwedzera pakati, kunyangwe iyo chaiyo triplet kuumbwa kunopera.
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Kurasika katatu kunodzidzisa neural network kuisa zvinhu zvakafanana padyo pamwe chete uye zvinhu zvakasiyana zviri kure munzvimbo yekumisikidza. Ndiwo hwaro kuseri kwekuzivikanwa kwechiso, kutsvaga mifananidzo, uye masisitimu ekurudziro anoda kuenzanisa zvinhu pane kungozvironga.
Katatu kane anchor, yakanaka inogovera kirasi yeancho, uye isina kunaka kubva kune imwe kirasi.
Pasina muganho, modhi yaigona mepu zvese kusvika panzvimbo imwechete, zvichiita kuti madaro ese ave zero uye zvishoma kugutsa kurasikirwa. Muganho unomanikidza kuparadzana chaiko.
Google's FaceNet yakashandisa kurasikirwa katatu kudzidza kusungirirwa kwechiso uye kuseta benchmark mukusimbisa kumeso.
Iyo modhi inoisa mamepu ekumisikidza mavheji; iwe wobva waenzanisa zvinhu nekuyera kureba pakati pekumisikidzwa kwavo.
Nekuti kucherechedzwa kunoenderana nekumisikidza chinhambwe, unogona kunyoresa chitupa chitsva nekungochengeta chakadzikwa, hapana modhi yekudzidzira inodiwa.
Ramba uchidzidza
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InoteveraGaidhi rinotevera
Distance Metrics in Machine Learning
Basics