{"id":"https://openalex.org/W4289830208","doi":"https://doi.org/10.1109/spcom55316.2022.9840811","title":"Low-level Bias discovery and Mitigation for Image Classification","display_name":"Low-level Bias discovery and Mitigation for Image Classification","publication_year":2022,"publication_date":"2022-07-11","ids":{"openalex":"https://openalex.org/W4289830208","doi":"https://doi.org/10.1109/spcom55316.2022.9840811"},"language":"en","primary_location":{"id":"doi:10.1109/spcom55316.2022.9840811","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spcom55316.2022.9840811","pdf_url":null,"source":{"id":"https://openalex.org/S4363608579","display_name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071891538","display_name":"Vartika Sengar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vartika Sengar","raw_affiliation_strings":["Embedded Devices and Intelligent Systems, TCS Research,India","Embedded Devices and Intelligent Systems, TCS Research, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research,India","institution_ids":[]},{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086338234","display_name":"B.S. Vivek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B.S. Vivek","raw_affiliation_strings":["Embedded Devices and Intelligent Systems, TCS Research,India","Embedded Devices and Intelligent Systems, TCS Research, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research,India","institution_ids":[]},{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017932558","display_name":"Gaurab Bhattacharya","orcid":"https://orcid.org/0000-0003-0244-2390"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gaurab Bhattacharya","raw_affiliation_strings":["Embedded Devices and Intelligent Systems, TCS Research,India","Embedded Devices and Intelligent Systems, TCS Research, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research,India","institution_ids":[]},{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077902462","display_name":"Jayavardhana Gubbi","orcid":"https://orcid.org/0000-0001-5833-1898"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jayavardhana Gubbi","raw_affiliation_strings":["Embedded Devices and Intelligent Systems, TCS Research,India","Embedded Devices and Intelligent Systems, TCS Research, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research,India","institution_ids":[]},{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072563444","display_name":"Arpan Pal","orcid":"https://orcid.org/0000-0001-9101-8051"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arpan Pal","raw_affiliation_strings":["Embedded Devices and Intelligent Systems, TCS Research,India","Embedded Devices and Intelligent Systems, TCS Research, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research,India","institution_ids":[]},{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036289245","display_name":"P Balamuralidhar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P Balamuralidhar","raw_affiliation_strings":["Embedded Devices and Intelligent Systems, TCS Research,India","Embedded Devices and Intelligent Systems, TCS Research, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research,India","institution_ids":[]},{"raw_affiliation_string":"Embedded Devices and Intelligent Systems, TCS Research, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1687,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.40617573,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9926000237464905,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9926000237464905,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9643999934196472,"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":0.9473000168800354,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7412176728248596},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7351372241973877},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6209186315536499},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.546533465385437},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5067148804664612},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46639201045036316},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3551939129829407},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32090798020362854}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7412176728248596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7351372241973877},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6209186315536499},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.546533465385437},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5067148804664612},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46639201045036316},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3551939129829407},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32090798020362854}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/spcom55316.2022.9840811","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spcom55316.2022.9840811","pdf_url":null,"source":{"id":"https://openalex.org/S4363608579","display_name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4321789545","https://openalex.org/W2601157893","https://openalex.org/W2131735617","https://openalex.org/W2373006798","https://openalex.org/W2056912418","https://openalex.org/W2123759770","https://openalex.org/W2033213769","https://openalex.org/W4312376745","https://openalex.org/W2136016640","https://openalex.org/W2082269393"],"abstract_inverted_index":{"Identification":[0],"of":[1,26,76,88,98,109,133,185,218],"bias":[2,27,32,48,178,219],"and":[3,52,119,136,221,231],"its":[4],"mitigation":[5,25,222],"in":[6,15,28,74,179],"a":[7,10,41,66,85,96,180,190],"classifier":[8,195],"is":[9,70,81,150,223],"fundamental":[11],"sanity":[12],"check":[13],"required":[14],"trustworthy":[16],"AI":[17],"systems.":[18],"There":[19],"have":[20,166,212],"been":[21],"many":[22],"methods":[23],"for":[24],"literature":[29],"that":[30,43,72,168,214],"use":[31],"as":[33],"apriori":[34],"information.":[35,146],"In":[36],"this":[37],"work,":[38],"we":[39,187],"propose":[40,189],"system":[42],"can":[44,171],"detect":[45,175],"the":[46,54,62,77,89,107,116,137,144,154,158,194,205,209,232],"low-level":[47],"(e.g.,":[49],"color,":[50],"texture)":[51],"mitigate":[53],"same.":[55],"A":[56],"novel":[57],"auto-encoder":[58,80],"architecture":[59],"to":[60,83,114,127,142,174,192,225],"explain":[61],"predictions":[63],"made":[64],"by":[65,92,105,152,162,196,203,208],"deep":[67],"neural":[68],"network":[69],"built":[71],"helps":[73],"identification":[75],"bias.":[78],"The":[79,122,147],"trained":[82,126,141],"produce":[84],"generalized":[86],"representation":[87],"input":[90],"image":[91,135,156],"decomposing":[93],"it":[94,198],"into":[95],"set":[97],"latent":[99],"embeddings.":[100,164],"These":[101],"embeddings":[102,124,139,160],"are":[103,125,140],"learned":[104,207],"specializing":[106],"group":[108],"higher":[110],"dimensional":[111],"feature":[112,148],"maps":[113],"learn":[115],"disentangled":[117],"color":[118,138,145,163],"shape":[120,123,159],"concepts.":[121],"reconstruct":[128],"discrete":[129],"wavelet":[130],"transform":[131],"components":[132],"an":[134],"capture":[143],"specialization":[149],"done":[151],"reconstructing":[153],"RGB":[155],"using":[157],"modulated":[161],"We":[165,211],"shown":[167,213],"these":[169],"representations":[170,206],"be":[172],"used":[173],"low":[176],"level":[177],"classification":[181],"task.":[182],"Post":[183],"detection":[184],"bias,":[186],"also":[188],"method":[191,217],"de-bias":[193],"training":[197],"with":[199],"counterfactual":[200],"images":[201],"generated":[202],"manipulating":[204],"auto-encoder.":[210],"our":[215],"proposed":[216,234],"discovery":[220],"able":[224],"achieve":[226],"state-of-the-art":[227],"results":[228],"on":[229],"ColorMNIST":[230],"newly":[233],"BiasedShape":[235],"dataset.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
