Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele
DenseNet ati ipon Asopọmọra adanwo
Test feature-map concatenation, growth rate, direct feature reuse, transition compression, gradient paths, and channel counts.
Ibeere 1 ti 8
Iṣẹ wo ni DenseNet nlo lati ṣajọpọ awọn maapu ẹya lati awọn ipele ti o ṣaju?
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Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.
- Iṣẹ wo ni DenseNet nlo lati ṣajọpọ awọn maapu ẹya lati awọn ipele ti o ṣaju?
- Ikanni-ọlọgbọn concatenation
- Ano-ọlọgbọn afikun
- Idipọ ti o pọju kọja awọn ipele
- Ilọpo-ọlọgbọn eroja
- Ni DenseNet, kini 'oṣuwọn idagbasoke' k n ṣakoso?
- Iṣeto oṣuwọn ẹkọ
- Awọn pooling window iwọn
- Nọmba awọn maapu ẹya tuntun ti Layer kọọkan n ṣe
- Awọn nọmba ti ipon ohun amorindun
- Bawo ni ọpọlọpọ taara awọn isopọ tẹlẹ laarin L fẹlẹfẹlẹ ni a ipon Àkọsílẹ?
- L
- L(L+1)/2
- 2L
- L onigun mẹrin
- Kini idi akọkọ ti awọn ipele iyipada ni DenseNet?
- Lati fi awọn ọna abuja to ku
- Lati mu iwọn idagba pọ si
- Lati lo yiyọ kuro nikan
- Lati downsample ati ki o din ẹya-ara-maapu iwọn laarin ipon awọn bulọọki
- Anfaani bọtini ti Asopọmọra ipon ni pe o ṣe iranlọwọ lati dinku iṣoro ikẹkọ wo?
- Overfitting to awọ
- Exploding ipele titobi
- Iṣoro isọdi-afẹ kuro
- Aami ariwo
- Ti a ṣe afiwe si ResNet ni deede kanna lori ImageNet, DenseNet-BC lo igbagbogbo:
- Awọn paramita ti o dinku pupọ
- Jina siwaju sii sile
- Nipa awọn paramita kanna
- Ko si convolutions ni gbogbo
- A late DenseNet layer needs an edge feature learned near the input. Why can it use that map directly?
- Only the final layer can see edge features
- DenseNet resets all maps after each layer
- Transition layers reconstruct every earlier map from scratch
- Earlier feature maps remain among its concatenated inputs
- A dense block begins with m channels and three layers each add k new maps. How many channels emerge before a transition?
- 3m + k
- m + 3k
- m times k cubed
- Only k, because old maps are discarded