Amanethiwekhi Asele
Amanethiwekhi Esalela (ResNets) amanethiwekhi ajulile e-neural angeza 'ukweqa ukuxhumana' okuvumela izendlalelo zifunde ukulungiswa okuncane esikhundleni sokuguqulwa okugcwele.
Uhlolojikelele
This simple trick made it possible to train networks hundreds of layers deep, sparking a leap in image recognition accuracy.
I-Deep Dive
Ngaphambi kwe-ResNets, ukupakisha izendlalelo eziningi ngokuxakayo kwenza amanethiwekhi enze kabi kakhulu, ngisho nakudatha yokuqeqeshwa, inkinga ebizwa ngokuthi ukuwohloka. Ngo-2015, Microsoft abacwaningi u-Kaiming He kanye nozakwabo bethula ibhulokhi eyinsalela: esikhundleni sokucela inqwaba yezendlalelo ukuthi ikhiqize okukhiphayo okungu-H(x) ngokuqondile, bayivumela ifunde okusele F(x) = H(x) - x, base bengeza okokufaka koqobo x emuva ngesinqamuleli. Uma ungqimba lungadingeki, lungavele lufunde ukungenzi lutho (F(x) = 0). I-ResNet-152 iwine umncintiswano we-ImageNet wango-2015 ngephutha eliphezulu-5 lamaphesenti angaba ngu-3.6, yehlula izilinganiso zezinga labantu, futhi ukwakheka kwayo kwaba umgogodla oyisisekelo wokutholwa, ukuhlukaniswa, nokuthatha izithombe zezokwelapha.
I-Technical Insight
Uxhumano lwe-skip lushintsha umsebenzi webhulokhi ngalinye ube ngu-y = F(x) + x. Ngesikhathi sokusakazwa ngemuva, i-gradient igeleza kusinqamuleli sobunikazi singashintshiwe, ngakho-ke asikwazi ukunyamalala siye eduze kweziro ngisho nakumakhulu ezendlalelo. Lokhu kugcina izitaki ezijulile ziqeqesheka. Izinqamuleli zomazisi awengezi amapharamitha engeziwe; kuphela uma osayizi bokufaka nokukhishwayo behluka lapho ukuqagela okuncane (1x1 convolution) kulungisa ubukhulu ngaphambi kokwengeza.
I-Strategic Impact
Isivinini nesikali
I-Visual AI ingakwazi ukuhlola, ukutholwa, nokumaka imisebenzi esikalini.
Yakha ukukhetha
Amathimba aqanjiwe angakwazi ukulinganisa imiqondo ngokushesha ngezibuyekezo ezimbalwa ezenziwa mathupha.
Ithimba kanye nokusebenza komsebenzi
Imisebenzi ingasebenzisa amasiginali wesithombe nawevidiyo obekunzima ukuwenza ngaphambilini.
Ikusasa Lamanethiwekhi Asele
Ukuxhumana okuyinsalela manje sekuseduze nendawo yonke: Iziguquli, amamodeli okusabalalisa, namamodeli amakhulu olimi konke kuwasebenzisa ukuze kuzinzise ukuqeqeshwa kwezitaki ezijule kakhulu. Ucwaningo luyaqhubeka ezinhlobonhlobo ezifana nokwenza kusebenze ngaphambilini i-ResNets, izindlela eziqoqwe ze-ResNeXt, kanye nokuhlanganisa imibono eyinsalela nokuqeqeshwa okungenakho ukujwayelekile. Lindela isimiso sokuxhumana sokweqa ukuze siqhubeke njengesivimbeli sokwakha esizenzakalelayo, njengoba nje izakhiwo ezizungezile zisuka ekuguquguqukeni okumsulwa ziye ekunakekelweni naseziklameni eziyingxubevange.
Ukuqaliswa Komhlaba Wangempela
I-ImageNet Classification Backbones (ResNet-50, ResNet-101) isetshenziswa njengezici eziqeqeshelwe kusengaphambili zokufunda ukudlulisa
Ukutholwa kwesimila nesilonda kuzithombe ze-radiology ne-pathology kusetshenziswa izishumeki ezisuselwe ku-ResNet
Ukutholwa kwento kanye nezibonelo zezinhlaka zesegimenti ezifana ne-Faster R-CNN kanye ne-Mask R-CNN esebenzisa i-backbones ye-ResNet
Amapayipi okubona ozishayelayo ahlukanisa abahamba ngezinyawo, izimoto, nezimpawu ezivela kumafreyimu ekhamera
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.
Hlola ngedatha efana nezimo zangempela zokukhiqiza.
Engeza isibuyekezo somuntu ukuze uthole ukuzethemba okuphansi noma izibikezelo zomthelela omkhulu.
Landelela ukukhukhuleka kwemodeli bese uqinisekisa kabusha ngemva kwezinguquko zekhamera noma zesethi yedatha.
Qhubeka Uhlole
Free newsletter
Get the daily AI briefing
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the Residual Networks quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Umhlahlandlela olandelayo
Faka ama-Pyramid Networks
Imibuzo evame ukubuzwa
What is Residual Networks?
Amanethiwekhi Esalela (ResNets) amanethiwekhi ajulile e-neural angeza 'ukweqa ukuxhumana' okuvumela izendlalelo zifunde ukulungiswa okuncane esikhundleni sokuguqulwa okugcwele. Leli qhinga elilula lenze kwaba nokwenzeka ukuqeqesha amanethiwekhi amakhulu ezendlalelo ezijulile, okuvusa ukunemba kokubonwa kwesithombe.
Iyiphi inkinga ukuxhuma okuyinsalela okuyixazulule ngokukhethekile?
Ngaphambi kwe-ResNets, ukungeza izendlalelo eziningi kubangele ukunemba kwehliswe ngisho nakudatha yokuqeqeshwa. Yeqa ukuxhumana okulungisile lokhu ngokwenza izendlalelo zibe lula ukuzilungiselela.
Ibhulokhi eyinsalela ihlanganisani ngempela njengokuphuma kwayo?
Imiphumela yebhulokhi eyinsalela y = F(x) + x, yengeza insalela efundiwe kokokufakayo ngoxhumano lokweqa.
Kungani ukweqa ukuxhumana kusiza ama-gradient ngesikhathi sokuqeqeshwa?
Isinqamuleli sikamazisi sinikeza indlela eqondile yokuthi ama-gradient agezele emuva angashintshiwe, avimbele inkinga eshabalalayo yegradient kuzitaki ezijulile kakhulu.
Cishe zingaki izendlalelo imodeli ewinile ye-ResNet kusukela ngo-2015?
I-ResNet-152, enezendlalelo ezingu-152, iwine umncintiswano we-ImageNet ka-2015, okubonisa ukuthi amanethiwekhi ajule kakhulu manje angaqeqeshwa ngempumelelo.
Uma izingqimba zebhulokhi eyinsalela zifunda u-F(x) = 0, lenzani ibhulokhi?
Uma u-F(x) = 0, okukhiphayo kungu-x nje, ngakho ibhulokhi iba imephu yobunikazi. Lokhu kwenza izendlalelo ezengeziwe zingabi nangozi uma zingadingeki.