Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets.
<p>In our proposed SAASNets detection framework, λ = 0.2.</p>
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2025
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| _version_ | 1852023150034812928 |
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| author | Shuai Pang (4447831) |
| author2 | Chaochao You (20642598) Min Zhang (111999) Baojie Zhang (511505) Liyou Wang (20642601) Xiaolong Shi (412458) Yu Sun (18463) |
| author2_role | author author author author author author |
| author_facet | Shuai Pang (4447831) Chaochao You (20642598) Min Zhang (111999) Baojie Zhang (511505) Liyou Wang (20642601) Xiaolong Shi (412458) Yu Sun (18463) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shuai Pang (4447831) Chaochao You (20642598) Min Zhang (111999) Baojie Zhang (511505) Liyou Wang (20642601) Xiaolong Shi (412458) Yu Sun (18463) |
| dc.date.none.fl_str_mv | 2025-01-30T18:37:40Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0306755.t005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Experimental_results_of_different_with_i_L_i_sub_i_s_i_sub_loss_function_on_the_CDD_datasets_/28317030 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Science Policy Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> interfered shared attention aggregation receptive field limits highly efficient channel head attention module cdd datasets show sharing channel information encode position details detecting different scales building change detection feature aggregation module scale local features irregular object buildings redundant information different stages detection performance feature extractor siamese network siamese features residual strategy position multi global features external factors experimental results encoding stage detailed semantics better accuracy |
| dc.title.none.fl_str_mv | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>In our proposed SAASNets detection framework, λ = 0.2.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_27eedcb10816c19a2464f985f433da3a |
| identifier_str_mv | 10.1371/journal.pone.0306755.t005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28317030 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets.Shuai Pang (4447831)Chaochao You (20642598)Min Zhang (111999)Baojie Zhang (511505)Liyou Wang (20642601)Xiaolong Shi (412458)Yu Sun (18463)NeuroscienceScience PolicySpace ScienceBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> interferedshared attention aggregationreceptive field limitshighly efficient channelhead attention modulecdd datasets showsharing channel informationencode position detailsdetecting different scalesbuilding change detectionfeature aggregation modulescale local featuresirregular object buildingsredundant informationdifferent stagesdetection performancefeature extractorsiamese networksiamese featuresresidual strategyposition multiglobal featuresexternal factorsexperimental resultsencoding stagedetailed semanticsbetter accuracy<p>In our proposed SAASNets detection framework, λ = 0.2.</p>2025-01-30T18:37:40ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0306755.t005https://figshare.com/articles/dataset/Experimental_results_of_different_with_i_L_i_sub_i_s_i_sub_loss_function_on_the_CDD_datasets_/28317030CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/283170302025-01-30T18:37:40Z |
| spellingShingle | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets. Shuai Pang (4447831) Neuroscience Science Policy Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> interfered shared attention aggregation receptive field limits highly efficient channel head attention module cdd datasets show sharing channel information encode position details detecting different scales building change detection feature aggregation module scale local features irregular object buildings redundant information different stages detection performance feature extractor siamese network siamese features residual strategy position multi global features external factors experimental results encoding stage detailed semantics better accuracy |
| status_str | publishedVersion |
| title | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets. |
| title_full | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets. |
| title_fullStr | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets. |
| title_full_unstemmed | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets. |
| title_short | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets. |
| title_sort | Experimental results of different λ with <i>L</i><sub><i>s</i></sub> loss function on the CDD datasets. |
| topic | Neuroscience Science Policy Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> interfered shared attention aggregation receptive field limits highly efficient channel head attention module cdd datasets show sharing channel information encode position details detecting different scales building change detection feature aggregation module scale local features irregular object buildings redundant information different stages detection performance feature extractor siamese network siamese features residual strategy position multi global features external factors experimental results encoding stage detailed semantics better accuracy |