Matches in SemOpenAlex for { <https://semopenalex.org/work/W2967200242> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W2967200242 abstract "Deep neural networks can be difficult to train and require extensive fine tuning for hyper-parameter optimization. In this paper a generalized deep convolutional hybrid network model is proposed, named Reception that not only can tackle problem of solving optimal kernel size but also have goodness of both ResNet and Inception. The proposed Reception module, compliments the learning of filters having small and large receptive fields. This allows the network to extract the tiniest of details as well as the broadest of shapes. Although this strategy increases the width of the network and the number of parameters, the depth requirement of the network reduces significantly. Moreover, the number of parameters are kept in line using a carefully crafted design. The model when used for classifying ships in satellite images achieves a mean test accuracy of 98.56% with standard deviation of 0.14 in 5-fold cross validation and F1-score of 0.99." @default.
- W2967200242 created "2019-08-22" @default.
- W2967200242 creator A5001639634 @default.
- W2967200242 creator A5011391042 @default.
- W2967200242 creator A5072243664 @default.
- W2967200242 creator A5079215564 @default.
- W2967200242 date "2019-06-01" @default.
- W2967200242 modified "2023-09-23" @default.
- W2967200242 title "Reception - A Deep Learning Based Hybrid Residual Network" @default.
- W2967200242 cites W1686810756 @default.
- W2967200242 cites W2004915807 @default.
- W2967200242 cites W2095705004 @default.
- W2967200242 cites W2097117768 @default.
- W2967200242 cites W2183341477 @default.
- W2967200242 cites W2194775991 @default.
- W2967200242 cites W2549139847 @default.
- W2967200242 cites W2949117887 @default.
- W2967200242 cites W2964121744 @default.
- W2967200242 cites W2964350391 @default.
- W2967200242 doi "https://doi.org/10.1109/sds.2019.00012" @default.
- W2967200242 hasPublicationYear "2019" @default.
- W2967200242 type Work @default.
- W2967200242 sameAs 2967200242 @default.
- W2967200242 citedByCount "1" @default.
- W2967200242 countsByYear W29672002422021 @default.
- W2967200242 crossrefType "proceedings-article" @default.
- W2967200242 hasAuthorship W2967200242A5001639634 @default.
- W2967200242 hasAuthorship W2967200242A5011391042 @default.
- W2967200242 hasAuthorship W2967200242A5072243664 @default.
- W2967200242 hasAuthorship W2967200242A5079215564 @default.
- W2967200242 hasConcept C108583219 @default.
- W2967200242 hasConcept C11413529 @default.
- W2967200242 hasConcept C114614502 @default.
- W2967200242 hasConcept C119857082 @default.
- W2967200242 hasConcept C153180895 @default.
- W2967200242 hasConcept C154945302 @default.
- W2967200242 hasConcept C155512373 @default.
- W2967200242 hasConcept C2944601119 @default.
- W2967200242 hasConcept C33923547 @default.
- W2967200242 hasConcept C41008148 @default.
- W2967200242 hasConcept C50644808 @default.
- W2967200242 hasConcept C74193536 @default.
- W2967200242 hasConcept C81363708 @default.
- W2967200242 hasConceptScore W2967200242C108583219 @default.
- W2967200242 hasConceptScore W2967200242C11413529 @default.
- W2967200242 hasConceptScore W2967200242C114614502 @default.
- W2967200242 hasConceptScore W2967200242C119857082 @default.
- W2967200242 hasConceptScore W2967200242C153180895 @default.
- W2967200242 hasConceptScore W2967200242C154945302 @default.
- W2967200242 hasConceptScore W2967200242C155512373 @default.
- W2967200242 hasConceptScore W2967200242C2944601119 @default.
- W2967200242 hasConceptScore W2967200242C33923547 @default.
- W2967200242 hasConceptScore W2967200242C41008148 @default.
- W2967200242 hasConceptScore W2967200242C50644808 @default.
- W2967200242 hasConceptScore W2967200242C74193536 @default.
- W2967200242 hasConceptScore W2967200242C81363708 @default.
- W2967200242 hasLocation W29672002421 @default.
- W2967200242 hasOpenAccess W2967200242 @default.
- W2967200242 hasPrimaryLocation W29672002421 @default.
- W2967200242 hasRelatedWork W2898848345 @default.
- W2967200242 hasRelatedWork W2901747085 @default.
- W2967200242 hasRelatedWork W2908530716 @default.
- W2967200242 hasRelatedWork W2916965824 @default.
- W2967200242 hasRelatedWork W2943539461 @default.
- W2967200242 hasRelatedWork W2949401153 @default.
- W2967200242 hasRelatedWork W2989991345 @default.
- W2967200242 hasRelatedWork W3010835472 @default.
- W2967200242 hasRelatedWork W3012435458 @default.
- W2967200242 hasRelatedWork W3035718760 @default.
- W2967200242 hasRelatedWork W3048719051 @default.
- W2967200242 hasRelatedWork W3091445043 @default.
- W2967200242 hasRelatedWork W3098739061 @default.
- W2967200242 hasRelatedWork W3110145168 @default.
- W2967200242 hasRelatedWork W3119287517 @default.
- W2967200242 hasRelatedWork W3121912046 @default.
- W2967200242 hasRelatedWork W3130653716 @default.
- W2967200242 hasRelatedWork W3134036510 @default.
- W2967200242 hasRelatedWork W3211448647 @default.
- W2967200242 hasRelatedWork W2843775498 @default.
- W2967200242 isParatext "false" @default.
- W2967200242 isRetracted "false" @default.
- W2967200242 magId "2967200242" @default.
- W2967200242 workType "article" @default.