{"id":"https://openalex.org/W7154325972","doi":"https://doi.org/10.48550/arxiv.2604.11279","title":"A Deep Equilibrium Network for Hyperspectral Unmixing","display_name":"A Deep Equilibrium Network for Hyperspectral Unmixing","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154325972","doi":"https://doi.org/10.48550/arxiv.2604.11279"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11279","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11279","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.11279","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133607745","display_name":"Chentong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chentong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133596785","display_name":"Jincheng Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Jincheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133553672","display_name":"Fei Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100332996","display_name":"Jie Chen","orcid":"https://orcid.org/0000-0003-1230-6034"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.005100000184029341,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.002099999925121665,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.879800021648407},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5824999809265137},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5105999708175659},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4431000053882599},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.39329999685287476},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35899999737739563},{"id":"https://openalex.org/keywords/constant","display_name":"Constant (computer programming)","score":0.35839998722076416},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3560999929904938}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.879800021648407},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7422999739646912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6164000034332275},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5824999809265137},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5105999708175659},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4431000053882599},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33660000562667847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33480000495910645},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C2988224531","wikidata":"https://www.wikidata.org/wiki/Q20830730","display_name":"Network structure","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27129998803138733}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11279","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11279","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11279","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11279","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":"Preprint"},"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":{"Hyperspectral":[0],"unmixing":[1,12,117],"(HU)":[2],"is":[3],"crucial":[4],"for":[5],"analyzing":[6],"hyperspectral":[7],"imagery,":[8],"yet":[9],"achieving":[10],"accurate":[11],"remains":[13],"challenging.":[14],"While":[15],"traditional":[16],"methods":[17],"struggle":[18],"to":[19,91],"effectively":[20],"model":[21],"complex":[22],"spectral-spatial":[23,40,93],"features,":[24],"deep":[25,66],"learning":[26],"approaches":[27],"often":[28],"lack":[29],"physical":[30],"interpretability.":[31],"Unrolling-based":[32],"methods,":[33],"despite":[34],"offering":[35],"network":[36,90],"interpretability,":[37],"inadequately":[38],"exploit":[39],"information":[41],"and":[42,47,102,108],"incur":[43],"high":[44],"memory":[45,122],"costs":[46],"numerical":[48],"precision":[49],"issues":[50],"during":[51],"backpropagation.":[52,104],"To":[53],"address":[54],"these":[55],"limitations,":[56],"we":[57],"propose":[58],"DEQ-Unmix,":[59],"which":[60],"reformulates":[61],"abundance":[62],"estimation":[63],"as":[64],"a":[65,87],"equilibrium":[67],"model,":[68],"enabling":[69],"efficient":[70,101],"constant-memory":[71,103],"training":[72],"via":[73],"implicit":[74,97],"differentiation.":[75],"It":[76],"replaces":[77],"the":[78,82],"gradient":[79],"operator":[80],"of":[81],"data":[83],"reconstruction":[84],"term":[85],"with":[86],"trainable":[88],"convolutional":[89],"capture":[92],"information.":[94],"By":[95],"leveraging":[96],"differentiation,":[98],"DEQ-Unmix":[99,114],"enables":[100],"Experiments":[105],"on":[106],"synthetic":[107],"two":[109],"real-world":[110],"datasets":[111],"demonstrate":[112],"that":[113],"achieves":[115],"superior":[116],"performance":[118],"while":[119],"maintaining":[120],"constant":[121],"cost.":[123]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-15T00:00:00"}
