Reseau yu des
Reseau Residual (ResNets) ay reso neuronal yu xóot lañu yuy yokk 'lëkkaloo yi' di may layer yi ñu jàng ay coppite yu ndaw ci barabu coppite yu mat sëkk.
Résumé
Kaf gu yomb gi taxna ñu mëna tàggat reso yu xóot ba téemeeri diisaay, loolu taxna ñu mëna xàmmee nataal ci anam wu jaar yoon.
Plongeur bu xóot
Laata ResNets, dajale ay couche yu bari ci anam wu wuute, dafa def reso yi di gëna bon, ba ci done yuñ tàggat, jafe-jafe bu ñuy woowe degradaasioŋ. Ci atum 2015, gëstukat yi Kaiming moom ak ay naataangoom dugal nañu bloku residual bi: ludul laaj benn xeetu diisaay ngir génne H(x) ci saasi, dañu ko bàyyi mu jàng residual F(x) = H(x) - x, ba noppi yokk x ci d. Sudee soxlawul benn couche, mën na jàng baña def dara (F(x) = 0). ResNet-152 moo jël ndam li ci ImageNet 2015 ak top-5 njuumte bu tollu ci 3.6 pursaa, raw xayma yu nit ñi def, ba noppi architecture bi nekkoon yax bu am solo ngir gis, xaaj, ak nataal pajum.
Gis-gis xarala
Lëkkaloo skip bi dafay soppi liggéeyu blok bu nekk ci y = F(x) + x. Bu ñuy backpropagation, gradient bi dafay jaar ci gaawaayu dàntite bi te du soppeeku, kon mënul ni mes ba jege zero doonte dafa jaar ci téemeeri couche. Loolu dafay tax ñu mëna tàggat stack yu xóot yi. Gaawaayu dàntite du yokk beneen paramet; sudee dayo duggal ak genn wuute, projection bu ndaw (1x1 convolution) dafay yamale dayo yi laataa ñuy yokk.
njeextalu pexe
Gaawaay ak yaatuwaay
Visual IA mën na otomatise saytu, gis ak etiketu liggéey ci eskaal.
Tabax tànneef
Ekipu kreatif yi mën nañu defar konsept yu gëna gaaw te duñu def lu bari ci loxo.
Ekip ak def liggéey
Liggéeyukaay yi mën nañu jëfandikoo siñaal nataal wala wideo yu jafewoon lool ci liggéey.
Ëlëgu reso residual yi
Lëkkaloo yi des leegi dañuy nuru lu ñépp bokk: Transformatër yi, modeli diffusion yi, ak modeli làkk yu mag yi, ñoom ñépp dañu leen jëfandikoo ngir dakkal tàggat stack yu xóot lool. Gëstu baa ngi wéy ci anam yu melni ResNets yu njëkk, yooni ResNeXt yuñ boole, ak boole xalaat yu des ak tàggat bu amul normalisasioŋ. Xaarandil ni njàngalem skip-connection mooy wéy di nekk bloku tabax buñ jagleel, doonte architecture yi ko wër dañu joge ci convolution yu sell yi dem ci jëmmal ak jëmmal hybrid.
Doxal ci àdduna dëgg
ImageNet xeetu yaxu ndigg (ResNet-50, ResNet-101) jëfandikoo ngir dindi màndarga yiñ tàggat bu njëkk ngir jàng toxal
Gis tumër ak lesion ci nataali radiologie ak pathologie ci jëfandikoo encodeur yu sukkandiko ci ResNet
Gis mbir ak misaal kaadar xaaj lu melni R-CNN bu gaaw ak Mask R-CNN yuy jëfandikoo yaxu ndigg ResNet
Tuyo yiy dawal seen bopp yuy xaaj doxkat yi, oto yi ak màndarga yi bawoo ci kaadaru kamera yi
Risk yi ak balustrade yi
Yelleefi nataal ak nangu mën na nekk risku yoon sudee fi ñu bawoo leerul.
Performance model bi mën na wuute ci leeraay bi, demographie bi ak environmaa bi.
Njuumteg positive yi mën nañu dem te kenn duko seetlu fileek xool wuñu buntu wóolu sa bopp.
Roadmap ngir samp gi
Mandargal kritërium nangug njub, woowaat ak njëgu njuumte.
Saytu ak done yu méngoo ak anam yi ñuy liggéeyee dëgg.
Yokk jàngat nit ngir xam fu wóorul dara wala am njeexital yu rëy.
Toppal model drift bi nga baaxal ko ginaaw bi kamera bi wala done yi soppeekoo.
Weyal di banneexu
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Gis bi ci topp
Reseau Piramid
Laaj yi ñuy faral di laaj
Luy Reseau Residuel?
Reseau Residual (ResNets) ay reso neuronal yu xóot lañu yuy yokk 'lëkkaloo yi' di may layer yi ñu jàng ay coppite yu ndaw ci barabu coppite yu mat sëkk. Kaf gu yomb gi taxna ñu mëna tàggat reso yu xóot ba téemeeri diisaay, loolu taxna ñu mëna xàmmee nataal ci anam wu jaar yoon.
Ban jafe-jafe la lëkkaloo residual yi saafara?
Laata ResNets, yokk yeneen layers moo waral njubte gi di wàññeeku ba ci done yiñ tàggat. Skip lëkkaloo yi saafara nañu lii ci yombal layers yi ngir gëna xéewale.
Lan mooy residual block bi muy génne?
Blok residuel dafay génne y = F(x) + x, yokk residual biñ jàng ci duggal gi jaaraleko ci lëkkaloo skip.
Lan moo waral lëkkaloo yi di jàppale gradient yi ci diiru tàggat yaram?
Gaawaayu dàntite bi dafay jox yoonu gradient yi ñu mëna dellu ginaaw te duñu soppiku, loolu mooy tere jafe-jafe gradient biy réer ci stack yu xóot lool yi.
Lu tollu ci ñaata couche la modelu ResNet bi jël raw gàddu gi ci 2015 amoon?
ResNet-152, ak 152 layers, moo jël ndam li ci ImageNet 2015, loolu dafay wane ni leegi mën nañu tàggat reso yu xóot yi ci anam wu jaar yoon.
Sudee ab diisaayu bloc residuel jàng F(x) = 0, lan la bloc bi di def?
Sudee F(x) = 0, génne gi dafay nekk x, kon blok bi dafay nekk kàrtu dàntite. Loolu dafay tax yeneen couche duñu lore sudee soxla wuñu leen.