{"id":"https://openalex.org/W7139950460","doi":"https://doi.org/10.48550/arxiv.2603.19145","title":"Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection","display_name":"Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139950460","doi":"https://doi.org/10.48550/arxiv.2603.19145"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19145","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19145","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.19145","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130226986","display_name":"Ruilin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ruilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078167436","display_name":"Heming Tian Jianhua Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Heming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130239145","display_name":"Xiufeng Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Xiufeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130224606","display_name":"Zheming Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Zheming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130246547","display_name":"Jie Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130246578","display_name":"Chenliang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chenliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130230035","display_name":"Xue Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xue","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8134999871253967,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8134999871253967,"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/T11448","display_name":"Face recognition and analysis","score":0.053199999034404755,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.020999999716877937,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.7699999809265137},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.6567000150680542},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6263999938964844},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.6096000075340271},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5864999890327454},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5120999813079834},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.48080000281333923},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.44449999928474426}],"concepts":[{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.7699999809265137},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.6567000150680542},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6263999938964844},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.6096000075340271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5925999879837036},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5864999890327454},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5763999819755554},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5120999813079834},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.48080000281333923},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4171000123023987},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3452000021934509},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3215000033378601},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3206999897956848},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2754000127315521},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19145","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19145","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.19145","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19145","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"Recent":[0],"paradigms":[1],"in":[2,35,49],"Random":[3],"Projection":[4],"Layer":[5],"(RPL)-based":[6],"continual":[7,51],"representation":[8,34,140],"learning":[9,52],"have":[10],"demonstrated":[11],"superior":[12,176],"performance":[13,177],"when":[14],"building":[15],"upon":[16],"a":[17,24,29,40,65,126,149],"pre-trained":[18,60],"model":[19],"(PTM).":[20],"These":[21],"methods":[22],"insert":[23],"randomly":[25,66],"initialized":[26,67],"RPL":[27,68,81,153],"after":[28],"PTM":[30,139],"to":[31,136,141,179],"enhance":[32],"feature":[33,91],"the":[36,50,80,95,100,108,122,138,146,156],"initial":[37],"stage.":[38,53],"Subsequently,":[39],"linear":[41,101],"classification":[42],"head":[43],"is":[44],"used":[45],"for":[46],"analytic":[47,97,160],"updates":[48,98],"However,":[54],"under":[55,72],"severe":[56],"domain":[57,74],"gaps":[58],"between":[59],"representations":[61],"and":[62],"target":[63],"domains,":[64],"exhibits":[69],"limited":[70],"expressivity":[71],"large":[73],"shifts.":[75],"While":[76],"largely":[77],"scaling":[78],"up":[79],"dimension":[82],"can":[83],"improve":[84],"expressivity,":[85],"it":[86],"also":[87],"induces":[88],"an":[89],"ill-conditioned":[90],"matrix,":[92],"thereby":[93],"destabilizing":[94],"recursive":[96],"of":[99,148,159],"head.":[102],"To":[103],"this":[104],"end,":[105],"we":[106],"propose":[107],"Stochastic":[109],"Continual":[110],"Learner":[111],"with":[112],"MemoryGuard":[113],"Supervisory":[114],"Mechanism":[115],"(SCL-MGSM).":[116],"Unlike":[117],"random":[118,134],"initialization,":[119],"MGSM":[120],"constructs":[121],"projection":[123],"layer":[124],"via":[125],"principled,":[127],"data-guided":[128],"mechanism":[129],"that":[130,173],"progressively":[131],"selects":[132],"target-aligned":[133],"bases":[135],"adapt":[137],"downstream":[142],"tasks.":[143],"This":[144],"facilitates":[145],"construction":[147],"compact":[150],"yet":[151],"expressive":[152],"while":[154],"improving":[155],"numerical":[157],"stability":[158],"updates.":[161],"Extensive":[162],"experiments":[163],"on":[164],"multiple":[165],"exemplar-free":[166],"Class":[167],"Incremental":[168],"Learning":[169],"(CIL)":[170],"benchmarks":[171],"demonstrate":[172],"SCL-MGSM":[174],"achieves":[175],"compared":[178],"state-of-the-art":[180],"methods.":[181]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-21T00:00:00"}
