{"id":"https://openalex.org/W4414621316","doi":"https://doi.org/10.3390/make7040110","title":"Attention-Guided Differentiable Channel Pruning for Efficient Deep Networks","display_name":"Attention-Guided Differentiable Channel Pruning for Efficient Deep Networks","publication_year":2025,"publication_date":"2025-09-29","ids":{"openalex":"https://openalex.org/W4414621316","doi":"https://doi.org/10.3390/make7040110"},"language":"en","primary_location":{"id":"doi:10.3390/make7040110","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040110","pdf_url":"https://www.mdpi.com/2504-4990/7/4/110/pdf?version=1759130441","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/4/110/pdf?version=1759130441","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119768123","display_name":"Anouar Chahbouni","orcid":null},"institutions":[{"id":"https://openalex.org/I81605866","display_name":"Sidi Mohamed Ben Abdellah University","ror":"https://ror.org/04efg9a07","country_code":"MA","type":"education","lineage":["https://openalex.org/I81605866"]}],"countries":["MA"],"is_corresponding":true,"raw_author_name":"Anouar Chahbouni","raw_affiliation_strings":["Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco"],"affiliations":[{"raw_affiliation_string":"Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco","institution_ids":["https://openalex.org/I81605866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117839364","display_name":"Khaoula El Manaa","orcid":null},"institutions":[{"id":"https://openalex.org/I81605866","display_name":"Sidi Mohamed Ben Abdellah University","ror":"https://ror.org/04efg9a07","country_code":"MA","type":"education","lineage":["https://openalex.org/I81605866"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Khaoula El Manaa","raw_affiliation_strings":["Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco"],"affiliations":[{"raw_affiliation_string":"Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco","institution_ids":["https://openalex.org/I81605866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119768124","display_name":"Yassine Abouch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yassine Abouch","raw_affiliation_strings":["DAKAI Laboratory, Nextronic by Aba Technology, Casablanca 20253, Morocco"],"affiliations":[{"raw_affiliation_string":"DAKAI Laboratory, Nextronic by Aba Technology, Casablanca 20253, Morocco","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080660885","display_name":"Imane El Manaa","orcid":null},"institutions":[{"id":"https://openalex.org/I81605866","display_name":"Sidi Mohamed Ben Abdellah University","ror":"https://ror.org/04efg9a07","country_code":"MA","type":"education","lineage":["https://openalex.org/I81605866"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Imane El Manaa","raw_affiliation_strings":["Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco"],"affiliations":[{"raw_affiliation_string":"Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco","institution_ids":["https://openalex.org/I81605866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117932397","display_name":"Badre Bossoufi","orcid":null},"institutions":[{"id":"https://openalex.org/I81605866","display_name":"Sidi Mohamed Ben Abdellah University","ror":"https://ror.org/04efg9a07","country_code":"MA","type":"education","lineage":["https://openalex.org/I81605866"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Badre Bossoufi","raw_affiliation_strings":["Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco"],"affiliations":[{"raw_affiliation_string":"Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco","institution_ids":["https://openalex.org/I81605866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011490776","display_name":"Mohammed El Ghzaoui","orcid":"https://orcid.org/0000-0003-3416-2246"},"institutions":[{"id":"https://openalex.org/I81605866","display_name":"Sidi Mohamed Ben Abdellah University","ror":"https://ror.org/04efg9a07","country_code":"MA","type":"education","lineage":["https://openalex.org/I81605866"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Mohammed El Ghzaoui","raw_affiliation_strings":["Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco"],"affiliations":[{"raw_affiliation_string":"Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco","institution_ids":["https://openalex.org/I81605866"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047787876","display_name":"Rachid El Alami","orcid":"https://orcid.org/0000-0002-8524-9053"},"institutions":[{"id":"https://openalex.org/I81605866","display_name":"Sidi Mohamed Ben Abdellah University","ror":"https://ror.org/04efg9a07","country_code":"MA","type":"education","lineage":["https://openalex.org/I81605866"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Rachid El Alami","raw_affiliation_strings":["Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco"],"affiliations":[{"raw_affiliation_string":"Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco","institution_ids":["https://openalex.org/I81605866"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5119768123"],"corresponding_institution_ids":["https://openalex.org/I81605866"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":5.3831,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.95825514,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"7","issue":"4","first_page":"110","last_page":"110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9988999962806702,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10964","display_name":"Wireless Communication Security Techniques","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.8331999778747559},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5911999940872192},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5594000220298767},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5486000180244446},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.5232999920845032},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5099999904632568},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46050000190734863},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4449000060558319},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.43849998712539673}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8331999778747559},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7088000178337097},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5911999940872192},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5594000220298767},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5486000180244446},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.5232999920845032},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5099999904632568},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4950000047683716},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46050000190734863},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4099000096321106},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36230000853538513},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7040110","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040110","pdf_url":"https://www.mdpi.com/2504-4990/7/4/110/pdf?version=1759130441","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:18a518fa723f42668fcaf70ed1be1dec","is_oa":true,"landing_page_url":"https://doaj.org/article/18a518fa723f42668fcaf70ed1be1dec","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 7, Iss 4, p 110 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7040110","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040110","pdf_url":"https://www.mdpi.com/2504-4990/7/4/110/pdf?version=1759130441","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414621316.pdf","grobid_xml":"https://content.openalex.org/works/W4414621316.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W2054658115","https://openalex.org/W2149298154","https://openalex.org/W2752782242","https://openalex.org/W2806990599","https://openalex.org/W2808168148","https://openalex.org/W2962851801","https://openalex.org/W2963363373","https://openalex.org/W3005595240","https://openalex.org/W3028304412","https://openalex.org/W3109212549","https://openalex.org/W3197252688","https://openalex.org/W4221057289","https://openalex.org/W4308874947","https://openalex.org/W4312443924","https://openalex.org/W4353046995","https://openalex.org/W4386076083","https://openalex.org/W4389046159","https://openalex.org/W4390722043","https://openalex.org/W4400034913","https://openalex.org/W4401809480","https://openalex.org/W4402137675","https://openalex.org/W4402753755","https://openalex.org/W4403826779","https://openalex.org/W4407148885","https://openalex.org/W4408359381","https://openalex.org/W4409627756","https://openalex.org/W4411053685"],"related_works":[],"abstract_inverted_index":{"Deploying":[0],"deep":[1],"learning":[2,112],"(DL)":[3],"models":[4],"in":[5],"real-world":[6],"environments":[7],"remains":[8],"a":[9,60,103,186],"major":[10],"challenge,":[11],"particularly":[12],"under":[13,192],"resource-constrained":[14],"conditions":[15],"where":[16],"achieving":[17],"both":[18],"high":[19,34],"accuracy":[20,37,162],"and":[21,51,124,136,142,151,211],"compact":[22],"architectures":[23],"is":[24,118],"essential.":[25],"While":[26],"effective,":[27],"Conventional":[28],"pruning":[29,75,128],"methods":[30],"often":[31],"suffer":[32],"from":[33],"computational":[35],"overhead,":[36,123],"degradation,":[38],"or":[39],"disruption":[40],"of":[41,134],"the":[42],"end-to-end":[43],"training":[44],"process,":[45],"limiting":[46],"their":[47],"practicality":[48],"for":[49,129,173],"embedded":[50],"real-time":[52],"applications.":[53],"We":[54],"present":[55],"Dynamic":[56,61],"Attention-Guided":[57,62],"Pruning":[58,65],"(DAGP),":[59],"Soft":[63],"Channel":[64],"framework":[66],"that":[67],"overcomes":[68],"these":[69],"limitations":[70],"by":[71],"embedding":[72],"learnable,":[73],"differentiable":[74],"masks":[76,84],"directly":[77],"within":[78],"convolutional":[79],"neural":[80],"networks":[81],"(CNNs).":[82],"These":[83,195],"act":[85],"as":[86],"implicit":[87],"attention":[88],"mechanisms,":[89],"adaptively":[90],"suppressing":[91],"non-informative":[92],"channels":[93],"during":[94],"training.":[95],"A":[96],"progressively":[97],"scheduled":[98],"L1":[99],"regularization,":[100],"activated":[101],"after":[102],"warm-up":[104],"phase,":[105],"enables":[106],"gradual":[107],"sparsity":[108,137],"while":[109],"preserving":[110],"early":[111],"capacity.":[113],"Unlike":[114],"prior":[115],"methods,":[116],"DAGP":[117],"retraining-free,":[119],"introduces":[120],"minimal":[121],"architectural":[122],"supports":[125],"optional":[126],"hard":[127],"deployment":[130],"efficiency.":[131],"Joint":[132],"optimization":[133],"classification":[135],"objectives":[138],"ensures":[139],"stable":[140],"convergence":[141],"task-adaptive":[143],"channel":[144],"selection.":[145],"Experiments":[146],"on":[147,168,185],"CIFAR-10":[148],"(VGG16,":[149],"ResNet56)":[150],"PlantVillage":[152],"(custom":[153],"CNN)":[154],"achieve":[155],"up":[156],"to":[157,200,204],"98.82%":[158],"FLOPs":[159],"reduction":[160],"with":[161,180],"gains":[163],"over":[164],"baselines.":[165],"Real-world":[166],"validation":[167],"an":[169],"enhanced":[170],"PlantDoc":[171],"dataset":[172],"agricultural":[174],"monitoring":[175],"achieves":[176],"60":[177],"ms":[178],"inference":[179],"only":[181],"2.00":[182],"MB":[183],"RAM":[184],"Raspberry":[187],"Pi":[188],"4,":[189],"confirming":[190],"efficiency":[191],"field":[193],"conditions.":[194],"results":[196],"illustrate":[197],"DAGP\u2019s":[198],"potential":[199],"scale":[201],"beyond":[202],"agriculture":[203],"diverse":[205],"edge-intelligent":[206],"systems":[207],"requiring":[208],"lightweight,":[209],"accurate,":[210],"deployable":[212],"models.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-13T14:20:09.374765","created_date":"2025-10-10T00:00:00"}