{"id":"https://openalex.org/W4416771494","doi":"https://doi.org/10.48550/arxiv.2511.20270","title":"DRL-Guided Neural Batch Sampling for Semi-Supervised Pixel-Level Anomaly Detection","display_name":"DRL-Guided Neural Batch Sampling for Semi-Supervised Pixel-Level Anomaly Detection","publication_year":2025,"publication_date":"2025-11-25","ids":{"openalex":"https://openalex.org/W4416771494","doi":"https://doi.org/10.48550/arxiv.2511.20270"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.20270","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.20270","pdf_url":"https://arxiv.org/pdf/2511.20270","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.20270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120569521","display_name":"Amirhossein Khadivi Noghredeh","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Noghredeh, Amirhossein Khadivi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106733832","display_name":"Abdollah Safari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Safari, Abdollah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114947681","display_name":"Fatemeh Ziaeetabar","orcid":"https://orcid.org/0000-0003-1159-3588"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ziaeetabar, Fatemeh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002043215","display_name":"Firoozeh Haghighi","orcid":"https://orcid.org/0000-0003-1880-937X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haghighi, Firoozeh","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5120569521"],"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.8199999928474426,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.8199999928474426,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.05420000106096268,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.03620000183582306,"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/overfitting","display_name":"Overfitting","score":0.7232000231742859},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7146999835968018},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6847000122070312},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6290000081062317},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.454800009727478},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41990000009536743},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40130001306533813},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.39820000529289246}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7610999941825867},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7232000231742859},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7146999835968018},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6847000122070312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6559000015258789},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6290000081062317},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3953999876976013},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3853999972343445},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37310001254081726},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.32339999079704285},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.28769999742507935},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26910001039505005},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2502000033855438}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.20270","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.20270","pdf_url":"https://arxiv.org/pdf/2511.20270","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.20270","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.20270","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.20270","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.20270","pdf_url":"https://arxiv.org/pdf/2511.20270","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Anomaly":[0],"detection":[1,32],"in":[2,28,84,138,142,150,152],"industrial":[3],"visual":[4],"inspection":[5],"is":[6],"challenging":[7],"due":[8],"to":[9,93],"the":[10,80,85,91,107,153],"scarcity":[11],"of":[12,33,121,136,148],"defective":[13,99],"samples.":[14],"Most":[15],"existing":[16],"methods":[17],"rely":[18],"on":[19,106],"unsupervised":[20],"reconstruction":[21],"using":[22],"only":[23],"normal":[24,97],"data,":[25],"often":[26],"resulting":[27],"overfitting":[29],"and":[30,52,65,98,118,140],"poor":[31],"subtle":[34,122],"defects.":[35],"We":[36],"propose":[37],"a":[38,46,53,68,145],"semi-supervised":[39],"deep":[40],"reinforcement":[41],"learning":[42],"framework":[43],"that":[44,112],"integrates":[45],"neural":[47],"batch":[48],"sampler,":[49],"an":[50,133],"autoencoder,":[51],"predictor.":[54],"The":[55,71],"RL-based":[56],"sampler":[57],"adaptively":[58],"selects":[59],"informative":[60],"patches":[61],"by":[62],"balancing":[63],"exploration":[64],"exploitation":[66],"through":[67],"composite":[69],"reward.":[70],"autoencoder":[72],"generates":[73],"loss":[74],"profiles":[75],"highlighting":[76],"abnormal":[77],"regions,":[78],"while":[79,128],"predictor":[81],"performs":[82],"segmentation":[83],"loss-profile":[86],"space.":[87],"This":[88],"interaction":[89],"enables":[90],"system":[92],"effectively":[94],"learn":[95],"both":[96],"patterns":[100],"with":[101,144],"limited":[102],"labeled":[103],"data.":[104],"Experiments":[105],"MVTec":[108],"AD":[109],"dataset":[110],"demonstrate":[111],"our":[113],"method":[114],"achieves":[115],"higher":[116],"accuracy":[117],"better":[119],"localization":[120],"anomalies":[123],"than":[124],"recent":[125],"state-of-the-art":[126],"approaches":[127],"maintaining":[129],"low":[130],"complexity,":[131],"yielding":[132],"average":[134],"improvement":[135],"0.15":[137],"F1_max":[139,151],"0.06":[141],"AUC,":[143],"maximum":[146],"gain":[147],"0.37":[149],"best":[154],"case.":[155]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-28T00:00:00"}
