{"id":"https://openalex.org/W3015340845","doi":"https://doi.org/10.3390/make2020006","title":"The Importance of Loss Functions for Increasing the Generalization Abilities of a Deep Learning-Based Next Frame Prediction Model for Traffic Scenes","display_name":"The Importance of Loss Functions for Increasing the Generalization Abilities of a Deep Learning-Based Next Frame Prediction Model for Traffic Scenes","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015340845","doi":"https://doi.org/10.3390/make2020006","mag":"3015340845"},"language":"en","primary_location":{"id":"doi:10.3390/make2020006","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2020006","pdf_url":"https://www.mdpi.com/2504-4990/2/2/6/pdf?version=1587450411","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/2/2/6/pdf?version=1587450411","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022002281","display_name":"Sandra Aigner","orcid":"https://orcid.org/0000-0002-1951-6213"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Sandra Aigner","raw_affiliation_strings":["TUM Department of Aerospace and Geodesy, Technical University of Munich, 80333 Munich, Germany"],"raw_orcid":"https://orcid.org/0000-0002-1951-6213","affiliations":[{"raw_affiliation_string":"TUM Department of Aerospace and Geodesy, Technical University of Munich, 80333 Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079258186","display_name":"Marco K\u00f6rner","orcid":"https://orcid.org/0000-0002-9186-4175"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Marco K\u00f6rner","raw_affiliation_strings":["TUM Department of Aerospace and Geodesy, Technical University of Munich, 80333 Munich, Germany"],"raw_orcid":"https://orcid.org/0000-0002-9186-4175","affiliations":[{"raw_affiliation_string":"TUM Department of Aerospace and Geodesy, Technical University of Munich, 80333 Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5022002281"],"corresponding_institution_ids":["https://openalex.org/I62916508"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":830,"currency":"EUR","value_usd":895},"fwci":0.1962,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.47869575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"2","issue":"2","first_page":"78","last_page":"98"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991000294685364,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991000294685364,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9955000281333923,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.993399977684021,"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/generalization","display_name":"Generalization","score":0.68928062915802},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6821050643920898},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.675753116607666},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6487376093864441},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5948588252067566},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5576573610305786},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5537447333335876},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5282021164894104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5118181109428406},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46934783458709717},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.44830644130706787},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35445141792297363},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34156984090805054},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24737593531608582},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16499191522598267}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.68928062915802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6821050643920898},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675753116607666},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6487376093864441},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5948588252067566},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5576573610305786},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5537447333335876},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5282021164894104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5118181109428406},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46934783458709717},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.44830644130706787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35445141792297363},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34156984090805054},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24737593531608582},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16499191522598267},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/make2020006","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2020006","pdf_url":"https://www.mdpi.com/2504-4990/2/2/6/pdf?version=1587450411","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:58b3690794c44895a6e8cc51afa25d08","is_oa":true,"landing_page_url":"https://doaj.org/article/58b3690794c44895a6e8cc51afa25d08","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 2, Iss 2, Pp 78-98 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-4990/2/2/6/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/make2020006","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"Text"},{"id":"pmh:oai:mediatum.ub.tum.de:node/1594059","is_oa":true,"landing_page_url":"https://mediatum.ub.tum.de/1594059","pdf_url":null,"source":{"id":"https://openalex.org/S4377196330","display_name":"mediaTUM  (Technical University of Munich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I62916508","host_organization_name":"Technical University of Munich","host_organization_lineage":["https://openalex.org/I62916508"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"},{"id":"pmh:oai:mediatum.ub.tum.de:node/1602508","is_oa":false,"landing_page_url":"https://mediatum.ub.tum.de/1602508","pdf_url":null,"source":{"id":"https://openalex.org/S4377196330","display_name":"mediaTUM  (Technical University of Munich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I62916508","host_organization_name":"Technical University of Munich","host_organization_lineage":["https://openalex.org/I62916508"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make2020006","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2020006","pdf_url":"https://www.mdpi.com/2504-4990/2/2/6/pdf?version=1587450411","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8399999737739563,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G4089046268","display_name":null,"funder_award_id":"16AVF2019A","funder_id":"https://openalex.org/F4320310476","funder_display_name":"Bundesministerium f\u00fcr Verkehr und Digitale Infrastruktur"}],"funders":[{"id":"https://openalex.org/F4320310476","display_name":"Bundesministerium f\u00fcr Verkehr und Digitale Infrastruktur","ror":"https://ror.org/00e3ns026"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3015340845.pdf","grobid_xml":"https://content.openalex.org/works/W3015340845.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1686810756","https://openalex.org/W2064675550","https://openalex.org/W2107775979","https://openalex.org/W2108598243","https://openalex.org/W2133665775","https://openalex.org/W2150066425","https://openalex.org/W2183341477","https://openalex.org/W2248556341","https://openalex.org/W2331128040","https://openalex.org/W2340897893","https://openalex.org/W2401640538","https://openalex.org/W2601686579","https://openalex.org/W2605135824","https://openalex.org/W2751683986","https://openalex.org/W2753614144","https://openalex.org/W2766527293","https://openalex.org/W2798788386","https://openalex.org/W2799055999","https://openalex.org/W2800283575","https://openalex.org/W2807242871","https://openalex.org/W2883706473","https://openalex.org/W2894314201","https://openalex.org/W2894961607","https://openalex.org/W2914470917","https://openalex.org/W2962785568","https://openalex.org/W2963019093","https://openalex.org/W2963610939","https://openalex.org/W2963981733","https://openalex.org/W2964254867","https://openalex.org/W2964303162","https://openalex.org/W2973966280","https://openalex.org/W2989708447","https://openalex.org/W2990326433","https://openalex.org/W3003490096","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W3135697610","https://openalex.org/W2371138613","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2048963458","https://openalex.org/W2171299904","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2239445980"],"abstract_inverted_index":{"This":[0],"paper":[1],"analyzes":[2],"in":[3],"detail":[4],"how":[5],"different":[6,182],"loss":[7,67,80],"functions":[8],"influence":[9],"the":[10,37,41,47,60,70,92,96,107,117,189],"generalization":[11,93],"abilities":[12,94],"of":[13,40,51,54,65,95,112,184],"a":[14,28,52,181],"deep":[15],"learning-based":[16],"next":[17,42],"frame":[18,43],"prediction":[19,25,190],"model":[20,26,61],"for":[21,109,204],"traffic":[22],"scenes.":[23],"Our":[24],"is":[27],"convolutional":[29],"long-short":[30],"term":[31],"memory":[32],"(ConvLSTM)":[33],"network":[34],"that":[35,139,161,177],"generates":[36],"pixel":[38,49],"values":[39,50],"after":[44],"having":[45],"observed":[46],"raw":[48],"sequence":[53],"four":[55,110],"past":[56],"frames.":[57],"We":[58],"trained":[59],"with":[62,133],"21":[63],"combinations":[64],"seven":[66],"terms":[68,81,86],"using":[69],"Cityscapes":[71],"Sequences":[72],"dataset":[73],"and":[74,123,149,154,167,199],"an":[75],"identical":[76],"hyper-parameter":[77],"setting.":[78],"The":[79,174],"range":[82],"from":[83],"pixel-error":[84],"based":[85],"to":[87,103,116,197,202],"adversarial":[88],"terms.":[89],"To":[90],"assess":[91],"resulting":[97],"models,":[98],"we":[99,186],"generated":[100],"predictions":[101],"up":[102,196,201],"20":[104],"time-steps":[105],"into":[106],"future":[108],"datasets":[111,194],"increasing":[113],"visual":[114],"distance":[115,165],"training":[118],"dataset\u2014KITTI":[119],"Tracking,":[120],"BDD100K,":[121],"UA-DETRAC,":[122],"KIT":[124],"AIS":[125],"Vehicles.":[126],"All":[127],"predicted":[128],"frames":[129],"were":[130],"evaluated":[131],"quantitatively":[132],"both":[134],"traditional":[135],"pixel-based":[136],"evaluation":[137,159],"metrics,":[138,160],"is,":[140,162],"mean":[141],"squared":[142],"error":[143],"(MSE),":[144],"peak":[145],"signal-to-noise":[146],"ratio":[147],"(PSNR),":[148],"structural":[150],"similarity":[151,172],"index":[152],"(SSIM),":[153],"recent,":[155],"more":[156],"advanced,":[157],"feature-based":[158],"Fr\u00e9chet":[163],"inception":[164],"(FID),":[166],"learned":[168],"perceptual":[169],"image":[170],"patch":[171],"(LPIPS).":[173],"results":[175],"show":[176],"solely":[178],"by":[179,195,200],"choosing":[180],"combination":[183],"losses,":[185],"can":[187],"boost":[188],"performance":[191],"on":[192],"new":[193],"55%,":[198],"50%":[203],"long-term":[205],"predictions.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
