Uhlolojikelele
Amamodeli akhiqizayo asuselwa kumaphuzu adala idatha ngokufunda i-gradient yokusabalalisa idatha - isiqondiso esenza noma iyiphi isampuli enomsindo ibukeke kakhulu njengedatha yangempela. Lokhu kubuka komsebenzi wamaphuzu kuhlanganisa amamodeli okusabalalisa ngezibalo ezihlukene ze-stochastic futhi kusekela amajeneretha ezithombe eziningi zesimanje.
Amamodeli Okukhiqiza Asuselwe Emaphuzuni awokugeleza komsebenzi okubonwa ngekhompuyutha atolika noma akhiqize imidiya ebonakalayo ukuze ihlaziywe, isebenze, futhi isungule.
I-Deep Dive
Esikhundleni sokumodela ngokuqondile, amamodeli asekelwe kumaphuzu afunda isikolo: igradient yokuminyana kwamathuba elogi ngokuphathelene nokokufaka. Ukwazi ukuthi iyiphi indlela yokugudluza isampula ukuze kwandiswe amathuba akhona kwanele ukukhiqiza idatha entsha. Umsebenzi ka-Yang Song kanye noStefano Ermon wango-2019 uqeqeshe inethiwekhi ukuze ilinganisele lesi sikolo kumaleveli amaningi omsindo kusetshenziswa ukufanisa amaphuzu we-denoising, kwase kwenziwa amasampula anamandla we-Langevin - enyathela amaphuzu ngokuphindaphindiwe futhi engeza umsindo omncane. Iphepha labo lango-2021 lamaphuzu-SDE libonise ukuthi amamodeli asuselwa kumaphuzu ahlukene anobuso obubili benqubo efanayo eqhubekayo echazwe isibalo sokuhlukanisa esitokisini. Okubalulekile ukuthi, yonke i-SDE ine-ODE 'yokugeleza kwamathuba' ahambisanayo abelana ngamamajini afanayo, evumela amathuba okuba khona kanye namasampula asheshayo.
I-Technical Insight
Ukulinganisa amaphuzu edatha ehlanzekile ngokuqondile kunzima lapho idatha iyingcosana, ngakho-ke imodeli iqeqeshwa kudatha ephazanyiswa umsindo we-Gaussian ezikalini eziningi. Ukufanisa amaphuzu we-denoising kunikeza ithagethi ethathekayo: isikolo sokusatshalaliswa komsindo silingana nesiqondisindlela somsindo esihlukaniswa nokuhluka komsindo, ngakho ukubikezela umsindo nokubikezela amaphuzu empeleni kuyinto efanayo. Ukusampula kuxazulula i-SDE yesikhathi esibuyela emuva (noma i-ODE yamathuba alinganayo) kusukela kumsindo we-Gaussian omsulwa.
I-Mastering Score-based Generative Models
Amamodeli akhiqizayo asuselwa kumaphuzu adala idatha ngokufunda i-gradient yokusabalalisa idatha - isiqondiso esenza noma iyiphi isampuli enomsindo ibukeke kakhulu njengedatha yangempela. Lokhu kubuka komsebenzi wamaphuzu kuhlanganisa amamodeli okusabalalisa ngezibalo ezihlukene ze-stochastic futhi kusekela amajeneretha ezithombe eziningi zesimanje. Amamodeli Okukhiqiza Asuselwe Emaphuzuni awokugeleza komsebenzi okubonwa ngekhompuyutha atolika noma akhiqize imidiya ebonakalayo ukuze ihlaziywe, isebenze, futhi isungule. Ukuze wakhe ukuqonda okujulile, phatha amamodeli akhiqizayo asekelwe ku-Score-based njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukuqagela, futhi uhlukanise lokho uhlelo olungakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa ukunemba kwebhalansi yebhalansi yamamodeli asekelwe ku-Score-Based Generative namaqiniso okusebenza njengekhwalithi yedatha, ukuhluka kokukhanya, nokuvumelana kwamalebula. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
I-Visual AI ingakwazi ukuhlola, ukutholwa, nokumaka imisebenzi esikalini. Ngesikhathi esifanayo, amalungelo ezithombe kanye nemvume kungaba ubungozi bomthetho uma ukutholakala kungacacile. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
I-Visual AI ingakwazi ukuhlola, ukutholwa, nokumaka imisebenzi esikalini.
I-Visual AI ingakwazi ukuhlola, ukutholwa, nokumaka imisebenzi esikalini. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Amathimba aqanjiwe angakwazi ukulinganisa imiqondo ngokushesha ngezibuyekezo ezimbalwa ezenziwa mathupha.
Amathimba aqanjiwe angakwazi ukulinganisa imiqondo ngokushesha ngezibuyekezo ezimbalwa ezenziwa mathupha. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imisebenzi ingasebenzisa amasiginali wesithombe nawevidiyo obekunzima ukuwenza ngaphambilini.
Imisebenzi ingasebenzisa amasiginali wesithombe nawevidiyo obekunzima ukuwenza ngaphambilini. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
I-Noise-Conditional Score Networks (NCSN) ekhiqiza ubuso obubonakalayo ngokulandela ama-gradients afundiwe nge-Langevin dynamics.
Ukwakhiwa kabusha kwesithombe sezokwelapha, njenge-MRI esheshisiwe, lapho amaphuzu afundiwe enza njengangaphambi kokugcwalisa idatha yokuskena engasampulanga kangako.
Ukukhiqizwa kwesakhiwo samangqamuzana namaprotheni ekutholweni kwezidakamizwa, ukumodela ukulungiselelwa kwe-athomu ye-3D ngokusatshalaliswa okusekelwe kumaphuzu.
Ukwakheka kwe-wave wave yomsindo lapho amamodeli wamaphuzu ezwakala ebhekise enkulumweni ehlanzekile noma emculweni, njengamavokhoda asekelwe ekusakazweni.
Amaphethini Okusebenzisa
Amamodeli Okukhiqiza Asuselwa kumaphuzu asebenza
I-Noise-Conditional Score Networks (NCSN) ekhiqiza ubuso obubonakalayo ngokulandela ama-gradients afundiwe nge-Langevin dynamics.
I-Noise-Conditional Score Networks (NCSN) ekhiqiza ubuso obubonakalayo ngokulandela ama-gradients afundiwe nge-Langevin Dynamics Teams ngokuvamile athola imiphumela engcono uma echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Amamodeli Okukhiqiza Asuselwa kumaphuzu asebenza
Ukwakhiwa kabusha kwesithombe sezokwelapha, njenge-MRI esheshisiwe, lapho amaphuzu afundiwe enza njengangaphambi kokugcwalisa idatha yokuskena engasampulanga kangako.
Ukwakhiwa kabusha kwesithombe sezokwelapha, njenge-MRI esheshisiwe, lapho amaphuzu afundiwe esebenza njengangaphambi kokugcwalisa idatha yokuskena enesampula encane Amathimba ngokuvamile athola imiphumela engcono uma echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka komuntu ezimweni ezibucayi, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Amamodeli Okukhiqiza Asuselwa kumaphuzu asebenza
Ukukhiqizwa kwesakhiwo samangqamuzana namaprotheni ekutholweni kwezidakamizwa, ukumodela ukulungiselelwa kwe-athomu ye-3D ngokusatshalaliswa okusekelwe kumaphuzu.
Ukukhiqizwa kwesakhiwo samangqamuzana namaphrotheni ekutholweni kwezidakamizwa, ukumodela ukulungiselelwa kwe-athomu ye-3D ngokusakazwa okusekelwe kumaphuzu Amathimba ngokuvamile athola imiphumela engcono uma echaza imikhawulo yekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Amamodeli Okukhiqiza Asuselwa kumaphuzu asebenza
Ukwakheka kwe-wave wave yomsindo lapho amamodeli wamaphuzu ezwakala ebhekise enkulumweni ehlanzekile noma emculweni, njengamavokhoda asekelwe ekusakazweni.
Ukwakheka kwegagasi lomsindo lapho amamodeli wamaphuzu ezwakala ebhekise enkulumweni ehlanzekile noma emculweni, njengakumavokhoda asekelwe ekusakazweni Amaqembu ngokuvamile athola imiphumela engcono lapho echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Amalungelo ezithombe kanye nemvume kungaba ubungozi bezomthetho uma ukuvela kungacacile.
Ukusebenza kwemodeli kungahluka kukho konke ukukhanya, izibalo zabantu, kanye nezindawo.
Okuhle okungelona iqiniso kungase kungabonakali ngaphandle uma izinga lokuzethemba liqashelwa.
Ukuqalisa Umhlahlandlela
Chaza indlela yokwamukela yokunemba, ukukhumbula, nezindleko zamaphutha.
Chaza indlela yokwamukela yokunemba, ukukhumbula, nezindleko zamaphutha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Hlola ngedatha efana nezimo zangempela zokukhiqiza.
Hlola ngedatha efana nezimo zangempela zokukhiqiza. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Engeza isibuyekezo somuntu ukuze uthole ukuzethemba okuphansi noma izibikezelo zomthelela omkhulu.
Engeza isibuyekezo somuntu ukuze uthole ukuzethemba okuphansi noma izibikezelo zomthelela omkhulu. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Landelela ukukhukhuleka kwemodeli bese uqinisekisa kabusha ngemva kwezinguquko zekhamera noma zesethi yedatha.
Landelela ukukhukhuleka kwemodeli bese uqinisekisa kabusha ngemva kwezinguquko zekhamera noma zesethi yedatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.