{"id":"https://openalex.org/W7118946912","doi":"https://doi.org/10.48550/arxiv.2601.01002","title":"Lightweight Channel Attention for Efficient CNNs","display_name":"Lightweight Channel Attention for Efficient CNNs","publication_year":2026,"publication_date":"2026-01-02","ids":{"openalex":"https://openalex.org/W7118946912","doi":"https://doi.org/10.48550/arxiv.2601.01002"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.01002","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01002","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.01002","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122159800","display_name":"Prem Babu Kanaparthi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kanaparthi, Prem Babu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122281933","display_name":"Tulasi Venkata Sri Varshini Padamata","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Padamata, Tulasi Venkata Sri Varshini","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5122159800"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8773999810218811,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8773999810218811,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.04270000010728836,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.019600000232458115,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6437000036239624},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6180999875068665},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6140000224113464},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.5891000032424927},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4546999931335449},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4302000105381012},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.40369999408721924},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.38530001044273376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7235999703407288},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6437000036239624},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6180999875068665},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6140000224113464},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.5891000032424927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4562000036239624},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4302000105381012},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.40369999408721924},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.38179999589920044},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30550000071525574},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C49289754","wikidata":"https://www.wikidata.org/wiki/Q2267081","display_name":"Side channel attack","level":3,"score":0.2549000084400177},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.01002","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01002","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.01002","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01002","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Attention":[0,45,52],"mechanisms":[1],"have":[2],"become":[3],"integral":[4],"to":[5,73],"modern":[6],"convolutional":[7],"neural":[8],"networks":[9],"(CNNs),":[10],"delivering":[11],"notable":[12],"performance":[13],"improvements":[14],"with":[15,70],"minimal":[16],"computational":[17],"overhead.":[18],"However,":[19],"the":[20],"efficiency":[21,106],"accuracy":[22],"trade":[23],"off":[24],"of":[25],"different":[26],"channel":[27],"attention":[28,80,129],"designs":[29],"remains":[30],"underexplored.":[31],"This":[32],"work":[33],"presents":[34],"an":[35],"empirical":[36],"study":[37],"comparing":[38],"Squeeze":[39],"and":[40,47,58,96,107,118],"Excitation":[41],"(SE),":[42],"Efficient":[43],"Channel":[44,51],"(ECA),":[46],"a":[48],"proposed":[49],"Lite":[50],"(LCA)":[53],"module":[54],"across":[55],"ResNet":[56,94],"18":[57,95],"MobileNetV2":[59],"architectures":[60],"on":[61,93,99],"CIFAR":[62],"10.":[63],"LCA":[64,86],"employs":[65],"adaptive":[66],"one":[67],"dimensional":[68],"convolutions":[69],"grouped":[71],"operations":[72],"reduce":[74],"parameter":[75,105,116],"usage":[76],"while":[77,101],"preserving":[78],"effective":[79],"behavior.":[81],"Experimental":[82],"results":[83],"show":[84],"that":[85],"achieves":[87],"competitive":[88],"accuracy,":[89],"reaching":[90],"94.68":[91],"percent":[92,98],"93.10":[97],"MobileNetV2,":[100],"matching":[102],"ECA":[103],"in":[104,132],"maintaining":[108],"favorable":[109],"inference":[110],"latency.":[111],"Comprehensive":[112],"benchmarks":[113],"including":[114],"FLOPs,":[115],"counts,":[117],"GPU":[119],"latency":[120],"measurements":[121],"are":[122],"provided,":[123],"offering":[124],"practical":[125],"insights":[126],"for":[127],"deploying":[128],"enhanced":[130],"CNNs":[131],"resource":[133],"constrained":[134],"environments.":[135]},"counts_by_year":[],"updated_date":"2026-01-08T20:10:11.968330","created_date":"2026-01-08T00:00:00"}
