{"id":"https://openalex.org/W7160949851","doi":"https://doi.org/10.48550/arxiv.2605.08618","title":"Beyond Toy Benchmarks: A Systematic Evaluation of OOD Detection Methods For Plant Pathology Classification","display_name":"Beyond Toy Benchmarks: A Systematic Evaluation of OOD Detection Methods For Plant Pathology Classification","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160949851","doi":"https://doi.org/10.48550/arxiv.2605.08618"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.08618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08618","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":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.2605.08618","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017998146","display_name":"Devesh Shah","orcid":"https://orcid.org/0009-0005-6891-2924"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shah, Devesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5017998146"],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.45669999718666077,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.45669999718666077,"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/T10616","display_name":"Smart Agriculture and AI","score":0.15219999849796295,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1298999935388565,"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/softmax-function","display_name":"Softmax function","score":0.7569000124931335},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6251000165939331},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5835999846458435},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5103999972343445},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4973999857902527},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.445499986410141},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41359999775886536},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.39579999446868896}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7569000124931335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6895999908447266},{"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/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6365000009536743},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6251000165939331},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5835999846458435},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5103999972343445},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C45237549","wikidata":"https://www.wikidata.org/wiki/Q1376796","display_name":"Restructuring","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3325999975204468},{"id":"https://openalex.org/C2777548347","wikidata":"https://www.wikidata.org/wiki/Q5456937","display_name":"Flagging","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C2777615720","wikidata":"https://www.wikidata.org/wiki/Q11888847","display_name":"Prioritization","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.2694999873638153}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.08618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08618","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.08618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08618","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":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":{"Out-of-distribution":[0],"(OOD)":[1],"detection":[2,32,65,123],"is":[3,124],"essential":[4],"for":[5],"reliable":[6],"deployment":[7],"of":[8,15,83,89],"deep":[9],"learning":[10],"systems,":[11],"yet":[12],"the":[13,45,67,84,90,138],"majority":[14],"existing":[16,115],"methods":[17,33,105],"are":[18,111],"evaluated":[19],"on":[20,44,126],"small,":[21],"visually":[22],"homogeneous":[23],"benchmarks.":[24],"In":[25],"this":[26],"work,":[27],"we":[28],"study":[29],"six":[30],"OOD":[31,62,122],"spanning":[34],"post-hoc":[35],"scoring,":[36],"auxiliary":[37],"objectives,":[38],"energy-based":[39],"models,":[40],"and":[41,130],"constrained":[42,103],"optimization":[43,104],"Plant":[46],"Pathology":[47],"2021":[48],"dataset,":[49],"a":[50,81],"fine-grained":[51],"task":[52],"with":[53],"natural":[54],"distribution":[55],"shifts.":[56],"Energy-based":[57],"fine-tuning":[58],"performs":[59],"best":[60],"across":[61],"settings,":[63],"improving":[64],"over":[66],"softmax":[68],"baseline":[69],"while":[70],"preserving":[71],"in-distribution":[72],"accuracy.":[73],"Analysis":[74],"shows":[75],"these":[76],"gains":[77],"stem":[78],"from":[79,114],"both":[80],"restructuring":[82],"embedding":[85],"space":[86],"alongside":[87],"calibration":[88],"scoring":[91],"function.":[92],"We":[93],"further":[94],"document":[95],"practical":[96],"training":[97],"instabilities":[98],"that":[99,110,120,131,140],"arise":[100],"when":[101],"scaling":[102],"to":[106],"moderate-sized":[107],"datasets,":[108],"findings":[109],"largely":[112],"absent":[113],"literature.":[116],"Our":[117],"results":[118],"demonstrate":[119],"principled":[121],"achievable":[125],"real-world":[127],"domain-specific":[128],"data":[129],"benchmark":[132],"evaluations":[133],"alone":[134],"may":[135],"not":[136],"capture":[137],"challenges":[139],"emerge":[141],"in":[142],"practice.":[143]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}
