{"id":"https://openalex.org/W4410538065","doi":"https://doi.org/10.1109/dcc62719.2025.00019","title":"Learned Compression for Compressed Learning","display_name":"Learned Compression for Compressed Learning","publication_year":2025,"publication_date":"2025-03-18","ids":{"openalex":"https://openalex.org/W4410538065","doi":"https://doi.org/10.1109/dcc62719.2025.00019"},"language":"en","primary_location":{"id":"doi:10.1109/dcc62719.2025.00019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dcc62719.2025.00019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Data Compression Conference (DCC)","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/A5093734257","display_name":"Dan Jacobellis","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dan Jacobellis","raw_affiliation_strings":["University of Texas at Austin,Austin,TX,USA,78712"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Austin,Austin,TX,USA,78712","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031510918","display_name":"Neeraja J. Yadwadkar","orcid":"https://orcid.org/0009-0007-7556-3069"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neeraja J. Yadwadkar","raw_affiliation_strings":["University of Texas at Austin,Austin,TX,USA,78712"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Austin,Austin,TX,USA,78712","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86519309"],"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":"113","last_page":"122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9810000061988831,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9810000061988831,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9645000100135803,"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/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.9018999934196472,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.6782784461975098},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6071596741676331},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.448797345161438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3491115868091583},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.14392906427383423},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.08277037739753723}],"concepts":[{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.6782784461975098},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6071596741676331},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.448797345161438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3491115868091583},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.14392906427383423},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.08277037739753723}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dcc62719.2025.00019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dcc62719.2025.00019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Data Compression Conference (DCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320307757","display_name":"Advanced Micro Devices","ror":"https://ror.org/04kd6c783"},{"id":"https://openalex.org/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"},{"id":"https://openalex.org/F4320309536","display_name":"University of Texas System","ror":"https://ror.org/01gek1696"},{"id":"https://openalex.org/F4320316620","display_name":"Amazon Catalyst","ror":"https://ror.org/04mv4n011"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W387761335","https://openalex.org/W2133665775","https://openalex.org/W2552465432","https://openalex.org/W2962785568","https://openalex.org/W2987748894","https://openalex.org/W3017136408","https://openalex.org/W3034469748","https://openalex.org/W3120043490","https://openalex.org/W3160506022","https://openalex.org/W3180355996","https://openalex.org/W4302067267","https://openalex.org/W4312806968","https://openalex.org/W4312847199","https://openalex.org/W4312933868","https://openalex.org/W4376464601","https://openalex.org/W4385815481","https://openalex.org/W4386590373","https://openalex.org/W4390871839","https://openalex.org/W4390873640","https://openalex.org/W4392909338","https://openalex.org/W4408345930","https://openalex.org/W6684191040","https://openalex.org/W6855830279","https://openalex.org/W6858005800","https://openalex.org/W6862995742"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2612632602","https://openalex.org/W2321805087"],"abstract_inverted_index":{"Modern":[0],"sensors":[1],"produce":[2],"increasingly":[3],"rich":[4],"streams":[5],"of":[6,20,81,168],"high-resolution":[7],"data.":[8,190],"Due":[9],"to":[10,31,84,149],"resource":[11],"constraints,":[12],"machine":[13],"learning":[14,28,186,197],"systems":[15,53],"discard":[16],"the":[17,133],"vast":[18],"majority":[19],"this":[21],"information":[22],"via":[23],"resolution":[24],"reduction.":[25],"Instead,":[26],"compressed-domain":[27,196],"allows":[29],"models":[30],"operate":[32],"on":[33],"compact":[34],"latent":[35,138],"representations.":[36],"However,":[37],"existing":[38],"compression":[39,52],"strategies":[40],"are":[41,220],"ill-suited":[42],"for":[43,177,195],"compressed":[44,189],"learning.":[45],"Linear":[46],"transform":[47,106],"coding":[48,107],"and":[49,118,160,175,180,207,214,217],"end-to-end":[50],"learned":[51],"reduce":[54,60,70],"bitrate,":[55],"but":[56,72],"do":[57],"not":[58,143],"uniformly":[59],"dimensionality.":[61],"Thus,":[62],"they":[63],"still":[64],"need":[65],"high":[66],"compute.":[67],"Generative":[68],"autoencoders":[69,134],"dimensionality,":[71],"their":[73],"adversarial":[74,147],"or":[75,146],"perceptual":[76,145],"objectives":[77],"result":[78],"in":[79,136],"loss":[80],"detail,":[82,152],"leading":[83],"decreased":[85],"model":[86],"quality.":[87],"To":[88],"address":[89],"these":[90],"limitations,":[91],"we":[92],"introduce":[93],"WaLLoC":[94,112,131,141],"(Wavelet":[95],"Learned":[96],"Lossy":[97],"Compression),":[98],"a":[99,114],"neural":[100],"codec":[101],"architecture":[102],"that":[103],"combines":[104],"linear":[105,169],"with":[108,155],"nonlinear":[109],"dimensionality-reducing":[110],"autoencoders.":[111],"sandwiches":[113],"shallow,":[115],"asymmetric":[116],"autoencoder":[117],"entropy":[119],"bottle-neck":[120],"between":[121],"an":[122],"invertible":[123],"wavelet":[124],"packet":[125],"transform.":[126],"Across":[127],"several":[128,199],"key":[129],"metrics,":[130],"outperforms":[132],"used":[135],"state-of-the-art":[137],"diffusion":[139],"models.":[140],"does":[142],"require":[144],"losses":[148],"represent":[150],"high-frequency":[151],"providing":[153],"compatibility":[154],"modalities":[156],"beyond":[157],"RGB":[158],"images":[159],"stereo":[161],"audio.":[162],"WaLLoC's":[163,193],"encoder":[164],"consists":[165],"almost":[166],"entirely":[167],"operations,":[170],"making":[171],"it":[172],"exceptionally":[173],"efficient":[174],"suitable":[176],"mobile":[178],"computing":[179],"remote":[181],"sensing.":[182],"It":[183],"also":[184],"enables":[185],"directly":[187],"from":[188],"We":[191],"demonstrate":[192],"capability":[194],"across":[198],"tasks,":[200],"including":[201],"image":[202,218],"classification,":[203],"colorization,":[204],"document":[205],"understanding,":[206],"music":[208],"source":[209],"separation.":[210],"Our":[211],"code,":[212],"experiments,":[213],"pre-trained":[215],"audio":[216],"codecs":[219],"available":[221],"at":[222],"https://ut-sysml.org/walloc/.":[223]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
