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DenseNet ak lëkkaloo bu dëgër
DenseNet reso convolutionnel la, fu layer bu nekk di jot kàrtu man-mani layer yi ko jiitu yépp ñuy dugal.
Déglul tegtal biiIA-nettali · ~2 min
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Résumé
This dense connectivity sharpens gradient flow, encourages feature reuse, and reaches strong accuracy with far fewer parameters than comparable deep networks.
Plongeur bu xóot
DenseNet, bi Huang, Liu, van der Maaten, ak Weinberger dugal ci 2017, dafay boole bépp etaas ak beneen etaas ci anam wu ñuy joxe kanam. Benn couche bu am L couche yu mat amna L (L + 1) / 2 lëkkaloo direct ci barabu L bi ñuy faral di def. Li gëna am solo mooy DenseNet dafay boole kàrtu màndarga yiy dugg moo gën ñu boole leen ni ko ResNet di defee, kon couche bu nekk dafay gis xam-xam bu mbooloo mi ci couche yu njëkk yépp te dafay jàppale ci limu new g = k2 32). Reseau bi dafa xaajaloo ay pàcc yu dëgër yuñ tàqale ay diisaay yuy wàcci. Design bii dafay yombal jafe-jafe gradient biy réer, dafay gëna dooleel tasaaroo man-man yi, ba noppi dafa am njariñ ci parametre yi: DenseNet-BC dafa méngoo ak njubteg ResNet ci ImageNet ak lu tollu ci ñatteelu pàcc ci parametre yi.
Doxal ci àdduna dëgg
Tuyo yiy jëfandikoo nataali pajum (lu melni, CheXNet ngir gis pneumonie) dañu tabax DenseNet-121 yaxu ndigg ngir xaaj rayon X yi ci dënn bi am sensitivite bu rëy.
Aplikaasioŋu mobile yiy xaaj feebaru gàñcax ak mbay mi dañuy jëfandikoo DenseNets yu kompact ndax dañuy am njubte bu baax ak ay paramet yu néew.
Satelit ak teledeteksioŋ ci xaaj bi ñuy muur suuf si dafay jëfandikoowaat màndarga yu dëgër ngir mëna xàmmee wuute yu am solo yi ci texture yi.
Xool biñ samp ci aparey yu am mémoire bu néew dafay jëfandikoo DenseNet-BC ngir am njubte bu tollu ci ResNet ci njëgu dencukaay bu woyof.
Ëlëgu DenseNet ak lëkkaloo bu dëgër
DenseNets yu sell yi ñoo gëna néew doole leegi ndax transformatëri gis-gis yi ak jëmmal ConvNeXt ñoo jiite benchmark yi, waaye lëkkaloo bu dëgër bi mingi wéy di am doole. Xalaatam ci boole dafa feeñaat ci yaxu ndigg yu baax, xeetu nataali medsin, ak dekodeer segmentation fu jëfandikoowaat man-man yi am solo ci biir budget memory bu sew. Xaarandil jëmmal hybrid yuy leble motif skip yu dëgër ngir aparey yu yam yi, boole ci wéyal jëfandikoo DenseNet variants fu done yuñ etiketee bariwul te efficacité parametre bi dafa ëpp balans bu ñor bi.
Teg ko ci jëf
Lu tax mu am solo
- Njëgg ak budget. Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
- dogal yu gëna leer. Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
- Xool kalite. Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Moytul
- Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
- Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
- Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Jéego yi ci topp
- Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
- Benchmark ci biir sargal ak done yu dëggu.
- Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
- Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Weyal di banneexu
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Nattal sa bopp
Take the DenseNet and Dense Connectivity quiz
Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.
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Laaj yi ñuy faral di laaj
What is DenseNet and Dense Connectivity?
DenseNet reso convolutionnel la, fu layer bu nekk di jot kàrtu man-mani layer yi ko jiitu yépp ñuy dugal. Lëkkaloo gu dëgër gii dafay gëna ñaw flow gradient bi, ñaax nit ñi ñu jëfandikoowaat man-man yi, ba noppi yegg ci njubte gu dëgër ak paramet yu néew lool yeneen reso yu xóot yiñ mëna méngale.
Ban jëf la DenseNet di jëfandikoo ngir boole kàrtu màndarga yi bawoo ci diisaay yi njëkk?
DenseNet dafay boole kàrtu màndarga yi ci bépp diisaay bu njëkk ci dimension chaine bi, wuute na ak ResNet bi leen di yokk.
Ci DenseNet, lan mooy 'taux de croissance' k?
Bépp couche du génne ludul k kàrtu màndarga yu bees, di tëye màggug chaine bi ci ligne ak model bi gëna dëgër.
Ñaata lëkkaloo yu direct ñoo am ci digganté L layers ci benn block bu dëgër?
Soo boole bépp couche ak couche yi ci topp dafay am L(L+1)/2 lëkkaloo yu jub.
Lan mooy jubluwaay bi gëna mag ci wàllu jàll ci DenseNet?
Couche transition yi dañuy jëfandikoo convolution 1x1 ak pooling ngir kompresse ak wàññi misaalu kàrtu màndarga yi ci diggante blok yu dëgër yi.
Njariñ li gëna mag ci lëkkaloo bu dëgër mooy, ban jafe-jafe tàggat yaram lay jàppale?
Lëkkaloo gi jub ci ñàkk gi dafay jox bépp couche yoonu gradient bu gàtt, yombal gradient yiy réer ba noppi may ñu saytu bu baax.
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