{"id":"https://openalex.org/W7213526626","doi":"https://doi.org/10.48550/arxiv.2609.19122","title":"Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation","display_name":"Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation","publication_year":2026,"publication_date":"2026-09-16","ids":{"openalex":"https://openalex.org/W7213526626","doi":"https://doi.org/10.48550/arxiv.2609.19122"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.19122","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.19122","pdf_url":"https://arxiv.org/pdf/2609.19122","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.19122","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121937118","display_name":"Meng\u2019en Qin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405255904","display_name":"Shenzhen University of Advanced Technology","ror":"https://ror.org/03hz5th67","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405255904"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin, Meng'en","raw_affiliation_strings":["Faculty of Computer Science and Artificial Intelligence , Shenzhen University of Advanced Technology , Shenzhen , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Artificial Intelligence , Shenzhen University of Advanced Technology , Shenzhen , China","institution_ids":["https://openalex.org/I4405255904"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5152016528","display_name":"Yinchen Liu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liu, Yinchen","raw_affiliation_strings":["School of Mathematical Sciences , University of Electronic Science and Technology of China , Chengdu , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences , University of Electronic Science and Technology of China , Chengdu , China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069576609","display_name":"Mei Cui","orcid":"https://orcid.org/0009-0002-8735-0400","observed_orcids":["https://orcid.org/0009-0002-8735-0400"]},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cui, Mingxuan","raw_affiliation_strings":["School of Mathematics , Shandong University , Jinan , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics , Shandong University , Jinan , China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5151266134","display_name":"Youlu Xing","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405255904","display_name":"Shenzhen University of Advanced Technology","ror":"https://ror.org/03hz5th67","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405255904"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing, Youlu","raw_affiliation_strings":["Faculty of Computer Science and Artificial Intelligence , Shenzhen University of Advanced Technology , Shenzhen , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Artificial Intelligence , Shenzhen University of Advanced Technology , Shenzhen , China","institution_ids":["https://openalex.org/I4405255904"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.7451000213623047,"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":0.7451000213623047,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.16990000009536743,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.012600000016391277,"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/convolutional-sparse-coding","display_name":"convolutional sparse coding","score":0.9850000143051147},{"id":"https://openalex.org/keywords/information-bottleneck","display_name":"information bottleneck","score":0.875},{"id":"https://openalex.org/keywords/adversarial-robustness","display_name":"adversarial robustness","score":0.7089999914169312},{"id":"https://openalex.org/keywords/fast-iterative-shrinkage-thresholding-algorithm","display_name":"FISTA","score":0.5049999952316284},{"id":"https://openalex.org/keywords/image-compression","display_name":"image compression","score":0.32499998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6674000024795532},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6538000106811523},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6013000011444092},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.574400007724762},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5145999789237976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5073000192642212},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45899999141693115},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.4512999951839447},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4302999973297119},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.41339999437332153},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.3880000114440918},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.3578000068664551},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.3077999949455261},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2897999882698059},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.28619998693466187},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.19122","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.19122","pdf_url":"https://arxiv.org/pdf/2609.19122","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.19122","doi":"https://doi.org/10.48550/arxiv.2609.19122","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.19122","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.19122","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.19122","pdf_url":"https://arxiv.org/pdf/2609.19122","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7213526626.pdf","grobid_xml":"https://content.openalex.org/works/W7213526626.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W1686946872"],"related_works":["https://openalex.org/W2890484421","https://openalex.org/W4398796724","https://openalex.org/W2981188038","https://openalex.org/W2171490498","https://openalex.org/W3082568708","https://openalex.org/W1946953458","https://openalex.org/W2618372886","https://openalex.org/W3201214182","https://openalex.org/W2048878808","https://openalex.org/W4417102400"],"abstract_inverted_index":{"Visual":[0],"signals":[1],"require":[2],"compact":[3,97],"yet":[4],"sufficient":[5],"representations":[6],"for":[7,19,45,123],"robust":[8,46],"downstream":[9],"prediction.":[10],"Convolutional":[11],"sparse":[12,42],"coding":[13,43],"(CSC)":[14],"provides":[15],"an":[16],"explicit":[17],"mechanism":[18],"suppressing":[20],"redundant":[21],"components":[22],"while":[23,99],"preserving":[24],"signal":[25,48,109],"content,":[26],"but":[27],"its":[28],"sparsity":[29,66,94],"coefficient":[30,67,84],"is":[31],"often":[32],"manually":[33],"selected":[34],"during":[35],"training.":[36],"We":[37,111],"propose":[38],"a":[39,69,114],"training-adaptive":[40],"convolutional":[41],"framework":[44],"visual":[47],"representation.":[49],"Specifically,":[50],"we":[51],"unfold":[52],"the":[53,57,65,75,79,86,93,100,120,127],"CSC":[54],"optimization":[55],"with":[56,74,104,126],"Fast":[58],"Iterative":[59],"Shrinkage-Thresholding":[60],"Algorithm":[61],"(FISTA)":[62],"and":[63,91,135,141],"treat":[64],"as":[68],"differentiable":[70],"variable":[71],"jointly":[72],"learned":[73],"network":[76,129],"parameters.":[77],"From":[78],"information":[80,89],"bottleneck":[81],"perspective,":[82],"this":[83],"controls":[85],"trade-off":[87],"between":[88],"retention":[90],"compression:":[92],"term":[95,102],"promotes":[96],"representations,":[98],"reconstruction":[101],"together":[103],"task":[105],"loss":[106],"preserves":[107],"task-relevant":[108],"content.":[110],"further":[112],"introduce":[113],"label-free":[115],"post-training":[116],"strategy":[117],"that":[118],"adjusts":[119],"compression":[121],"strength":[122],"corrupted":[124],"inputs":[125],"main":[128],"parameters":[130],"fixed.":[131],"Experiments":[132],"on":[133],"CIFAR":[134],"ImageNet":[136],"demonstrate":[137],"competitive":[138],"clean-data":[139],"recognition":[140],"greatly":[142],"improved":[143],"robustness":[144],"under":[145],"different":[146],"input":[147],"perturbations.":[148]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-09-18T00:00:00"}