{"id":"https://openalex.org/W7102403594","doi":"https://doi.org/10.48550/arxiv.2510.24105","title":"Enhancing Pre-trained Representation Classifiability can Boost its Interpretability","display_name":"Enhancing Pre-trained Representation Classifiability can Boost its Interpretability","publication_year":2025,"publication_date":"2025-10-28","ids":{"openalex":"https://openalex.org/W7102403594","doi":"https://doi.org/10.48550/arxiv.2510.24105"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2510.24105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.24105","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.2510.24105","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Shen, Shufan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Shufan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Qi, Zhaobo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Zhaobo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sun, Junshu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Junshu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Huang, Qingming","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Qingming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tian, Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wang, Shuhui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuhui","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":true,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9763000011444092,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9763000011444092,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0052999998442828655,"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":0.004600000102072954,"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/interpretability","display_name":"Interpretability","score":0.9962999820709229},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.7955999970436096},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.7754999995231628},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.37560001015663147},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3296999931335449}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9962999820709229},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.7955999970436096},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.7754999995231628},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5774000287055969},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5332000255584717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4390000104904175},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3912000060081482},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34040001034736633},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.289900004863739},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26969999074935913},{"id":"https://openalex.org/C114645060","wikidata":"https://www.wikidata.org/wiki/Q5431294","display_name":"Faithful representation","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2510.24105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.24105","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.2510.24105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.24105","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":{"The":[0,193],"visual":[1,19],"representation":[2,26,50,111,118],"of":[3,59,105,116,163],"a":[4],"pre-trained":[5,18,34,67,158,212],"model":[6],"prioritizes":[7],"the":[8,14,33,49,57,63,66,70,79,92,99,103,110,114,117,127,148,157,161,176,180,205],"classifiability":[9,41,130,138,162,177,210],"on":[10,87,186],"downstream":[11],"tasks,":[12],"while":[13],"widespread":[15],"applications":[16,199],"for":[17,25,179,211],"models":[20],"have":[21],"posed":[22],"new":[23],"requirements":[24],"interpretability.":[27,112],"However,":[28],"it":[29],"remains":[30],"unclear":[31],"whether":[32],"representations":[35,135,164],"can":[36,73,144,165,203],"achieve":[37],"high":[38],"interpretability":[39,51,119,128,172,208],"and":[40,108,129,197,209],"simultaneously.":[42],"To":[43],"answer":[44],"this":[45,88],"question,":[46],"we":[47,90,123,182],"quantify":[48],"by":[52,76,169],"leveraging":[53],"its":[54],"correlation":[55,196],"with":[56,120,136,171,175,189],"ratio":[58,104],"interpretable":[60,71,106,141],"semantics":[61,72,142],"within":[62],"representations.":[64,159],"Given":[65],"representations,":[68,181],"only":[69],"be":[74,145,166],"captured":[75,146],"interpretations,":[77],"whereas":[78],"uninterpretable":[80],"part":[81],"leads":[82],"to":[83,156],"information":[84,100],"loss.":[85],"Based":[86],"fact,":[89],"propose":[91],"Inherent":[93],"Interpretability":[94],"Score":[95],"(IIS)":[96],"that":[97,126,143,201],"evaluates":[98],"loss,":[101],"measures":[102],"semantics,":[107],"quantifies":[109],"In":[113],"evaluation":[115],"different":[121],"classifiability,":[122],"surprisingly":[124],"discover":[125],"are":[131,216],"positively":[132],"correlated,":[133],"i.e.,":[134],"higher":[137],"provide":[139],"more":[140],"in":[147,207],"interpretations.":[149],"This":[150],"observation":[151],"further":[152,167],"supports":[153],"two":[154],"benefits":[155],"First,":[160],"improved":[168],"fine-tuning":[170],"maximization.":[173],"Second,":[174],"improvement":[178],"obtain":[183],"predictions":[184],"based":[185],"their":[187],"interpretations":[188],"less":[190],"accuracy":[191],"degradation.":[192],"discovered":[194],"positive":[195],"corresponding":[198],"show":[200],"practitioners":[202],"unify":[204],"improvements":[206],"vision":[213],"models.":[214],"Codes":[215],"available":[217],"at":[218],"https://github.com/ssfgunner/IIS.":[219]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-30T00:00:00"}