{"id":"https://openalex.org/W7152669034","doi":"https://doi.org/10.48550/arxiv.2604.06893","title":"Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models","display_name":"Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7152669034","doi":"https://doi.org/10.48550/arxiv.2604.06893"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.06893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06893","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.06893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Devynck, Tom","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Devynck, Tom","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Faye, Bilal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Faye, Bilal","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Bouchaffra, Djamel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bouchaffra, Djamel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lazaar, Nadjib","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lazaar, Nadjib","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Azzag, Hanane","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azzag, Hanane","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Lebbah, Mustapha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lebbah, Mustapha","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5697000026702881,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5697000026702881,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.12250000238418579,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10499999672174454,"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/robustness","display_name":"Robustness (evolution)","score":0.6161999702453613},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.609499990940094},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.49729999899864197},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4909999966621399},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4424999952316284},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.4401000142097473},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4016999900341034},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.39399999380111694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6970999836921692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.678600013256073},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6161999702453613},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.609499990940094},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.49729999899864197},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44699999690055847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4424999952316284},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4016999900341034},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C14961307","wikidata":"https://www.wikidata.org/wiki/Q5377176","display_name":"Energy minimization","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2703999876976013}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.06893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06893","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.06893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06893","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":"article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8193690776824951,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"convolutional":[1,68,128],"neural":[2],"networks":[3],"achieve":[4],"remarkable":[5],"performance":[6],"by":[7],"exhaustively":[8],"processing":[9],"dense":[10],"spatial":[11,91,145],"feature":[12,52],"maps,":[13],"yet":[14],"this":[15],"brute-force":[16],"strategy":[17],"introduces":[18],"significant":[19],"computational":[20],"redundancy":[21],"and":[22,37,88,130,142],"encourages":[23],"reliance":[24],"on":[25,105,127],"spurious":[26],"background":[27],"correlations.":[28],"As":[29],"a":[30,47,55,62,75,89],"result,":[31],"modern":[32],"vision":[33],"models":[34],"remain":[35],"brittle":[36],"difficult":[38],"to":[39,113,121,139],"interpret.":[40],"We":[41,124],"propose":[42],"Energy-Regularized":[43],"Spatial":[44],"Masking":[45],"(ERSM),":[46],"novel":[48],"framework":[49],"that":[50,98,132,154,174],"reformulates":[51],"selection":[53],"as":[54,169],"differentiable":[56],"energy":[57,77,157],"minimization":[58],"problem.":[59],"By":[60],"embedding":[61],"lightweight":[63],"Energy-Mask":[64],"Layer":[65],"inside":[66],"standard":[67],"backbones,":[69],"each":[70,122],"visual":[71],"token":[72],"is":[73],"assigned":[74],"scalar":[76],"composed":[78],"of":[79],"two":[80],"competing":[81],"forces:":[82],"an":[83,116,170],"intrinsic":[84,171],"Unary":[85],"importance":[86,107],"cost":[87],"Pairwise":[90],"coherence":[92],"penalty.":[93],"Unlike":[94],"prior":[95],"pruning":[96,162],"methods":[97],"enforce":[99],"rigid":[100],"sparsity":[101],"budgets":[102],"or":[103],"rely":[104],"heuristic":[106],"scores,":[108],"ERSM":[109,126,168],"allows":[110],"the":[111,155],"network":[112],"autonomously":[114],"discover":[115],"optimal":[117],"information-density":[118],"equilibrium":[119],"tailored":[120],"input.":[123],"validate":[125],"architectures":[129],"demonstrate":[131],"it":[133],"produces":[134],"emergent":[135],"sparsity,":[136],"improved":[137],"robustness":[138,165],"structured":[140],"occlusion,":[141],"highly":[143],"interpretable":[144],"masks,":[146],"while":[147],"preserving":[148],"classification":[149],"accuracy.":[150],"Furthermore,":[151],"we":[152],"show":[153],"learned":[156],"ranking":[158],"significantly":[159],"outperforms":[160],"magnitude-based":[161],"in":[163],"deletion-based":[164],"tests,":[166],"revealing":[167],"denoising":[172],"mechanism":[173],"isolates":[175],"semantic":[176],"object":[177],"regions":[178],"without":[179],"pixel-level":[180],"supervision.":[181]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-04-10T00:00:00"}
