InoteveraGaidhi rinotevera
Dense Passage Retrieval
Mutauro AI
Nhungamiro yehunyanzvi
DenseNet uye Dense Kubatana
DenseNet ndeye convolutional network uko yese layer inogamuchira iyo mamepu eese akatangira maseru sekuisa.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
Kubatana uku kunorodza kuyerera kwe gradient, kunokurudzira kushandiswa zvakare, uye inosvika pachokwadi chakasimba nemaparamita mashoma kupfuura anofananidzwa akadzika network.
Kudzika Kwakadzika
DenseNet, yakaunzwa naHuang, Liu, van der Maaten, uye Weinberger muna 2017, inobatanidza imwe neimwe layer kune imwe neimwe layer mune yekudyisa-mberi fashoni. A layer ine L yakazara layers ine L(L+1)/2 yakananga yakabatana panzvimbo peyakajairika L. Crucially, DenseNet concatenates anouya maficha mamepu pane kupfupisa iwo sezvinoitwa neResNet, saka yega yega inoona ruzivo rwakabatanidzwa rwese ekutanga maseru uye inopa chete nhamba shoma yemamepu matsva (kukura kwayo, kazhinji k=12 kana 32). Iyo network inopatsanurwa kuita zvidhinha zvakadzika zvakapatsanurwa neshanduko layers iyo downsample. Dhizaini iyi inorerutsa dambudziko rekunyangarika-gradient, inosimbisa kupararira, uye yakanyanya parameter-inoshanda: DenseNet-BC inofananidzwa neResNet kurongeka paImageNet neinosvika chikamu chimwe muzvitatu chemaparamita.
Kuitwa muNyika yeChokwadi
Mapaipi ekufungidzira ekurapa (semuenzaniso, CheXNet yekuona mabayo) akavaka DenseNet-121 musana wekuisa chipfuva X-rays nekunzwa kwakanyanya.
Chirwere chekudyara uye kupatsanurwa kwembeu mbozha nhare dzinoshandisa compact DenseNets nekuti dzinorova chokwadi nema paramita mashoma.
Satellite uye kure-inonzwa pasi-kavha yemhando inokwirisa dense chimiro kushandiswa zvakare kusiyanisa zvisingaoneki magadzirirwo mutsauko.
Chiono chakamisikidzwa pane ndangariro-chinogumira zvishandiso zvinoshandisa DenseNet-BC akasiyana kuti awane ResNet-level kurongeka pamutengo wakaderera wekuchengetedza.
Ramangwana reDenseNet uye Dense Kubatana
Pure DenseNets haichanyanyi kutonga iye zvino izvo zvinoshandura chiratidzo uye ConvNeXt-maitiro magadzirirwo anotungamira mabhenji, asi dense yekubatanidza inoramba ine simba. Zano rayo rekubatanidza rinoonekwazve mumashure anoshanda, ekurapa-yekufungidzira modhi, uye segmentation decoder uko inoratidzira nyaya dzekushandisa zvakare pasi pemabhajeti akaomesesa ekurangarira. Tarisira madhizaini akasanganiswa anokwereta skip mapatani akaomesesa emidziyo yemupendero, pamwe nekuenderera mberi nekushandiswa kweDenseNet akasiyana apo data rakanyorwa riri kushomeka uye kushanda zvakanaka kweparameter kunodarika chikero chisina kujeka.
Kuzvishandisa
Nei zvichikosha
- Mutengo uye bhajeti. Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
- Sarudzo dzakajeka. Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
- Kudzora kwemhando yepamusoro. Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Chenjera
- Kugadzirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba.
- Infrastructure uye mari yekugadzirisa inowanzotarisirwa pasi.
- Chengetedzo uye kucherechedzwa mapundu anogona kukura sezvo masisitimu anowedzera kuoma.
Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Ramba Uchiongorora
Tsamba yemahara
Tora iyo yemazuva ese AI pfupiso
Nhatu dzakasimbiswa dzeAI nyaya mangwanani ega ega pakati pevhiki, dzakanyorwa neChirungu chakajeka. Yemahara zvachose, hapana ads.
Imwe email zuva rega rega revhiki. Bvisa kunyoresa kamwe chete. Hatimbotengese kana kugovera kero yako.
Zviedze iwe pachako
Take the DenseNet and Dense Connectivity quiz
Mhinduro yepakarepo pamhinduro yega yega, uye chitupa chinogovaniswa chine ID inobatika kana wapedza kosi.
Tsigira dzidzo yemahara yeAI. AI Understanding ndeye 501(c)(3) isingabatsiri - hapana ads, hapana paywall, zvachose. Ita chipo
Mibvunzo inowanzo bvunzwa
Chii chinonzi DenseNet uye Dense Kubatana?
DenseNet ndeye convolutional network uko yese layer inogamuchira iyo mamepu eese akatangira maseru sekuisa. Kubatana uku kunorodza kuyerera kwe gradient, kunokurudzira kushandiswa zvakare, uye inosvika pachokwadi chakasimba nemaparamita mashoma kupfuura anofananidzwa akadzika network.
Ndeipi mashandiro anoshandiswa neDenseNet kusanganisa mamepu kubva kumatanho apfuura?
DenseNet inosanganisa iyo mepu yemamepu ese ekutanga akaturikidzana padivi pechiteshi, kusiyana neResNet iyo inovawedzera.
MuDenseNet, 'chiyero chekukura' k chinodzora chii?
Imwe neimwe layer inoburitsa chete k mitsva yemamepu, ichichengeta chiteshi kukura mutsetse uye modhi compact.
Mangani akabatana akananga aripo pakati peL layers mune dense block?
Kubatanidza yese layer kune ese anotevera layer kunoburitsa L(L+1)/2 direct connections.
Ndechipi chinangwa chikuru cheshanduko maseru muDenseNet?
Shanduko maseru anoshandisa 1x1 convolution uye kubatanidza kudzvanya uye pasi sampuli mamepu emhando pakati pemabhuraki akakomba.
Yakakosha bhenefiti yedense yekubatanidza ndeyekuti inobatsira kudzikisira ndeipi dambudziko rekudzidzisa?
Kubatana kwakananga kune kurasikirwa kunopa yega yega nzira ipfupi gradient, kurerutsa kunyangarika magradients uye kugonesa kutarisisa kwakadzama.
Ramba uchidzidza
Mazano anoenderana
Mamwe madhairekitori akasarudzirwa nyaya iyi