Continual learning fault diagnosis: A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments Article Swipe
Bojian Chen
,
Changqing Shen
,
Juanjuan Shi
,
Lin Kong
,
Luyang Tan
,
Dong Wang
,
Zhongkui Zhu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.cja.2022.08.019
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.cja.2022.08.019
As a data-driven approach, deep learning (DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments. Therefore, adequately collecting fault data in advance is difficult. Limited by the characteristics of DL, training existing models directly with new fault data of new submachines leads to catastrophic forgetting of old tasks, while the cost of collecting all known data to retrain the models is excessively high. DL-based fault diagnosis methods cannot learn continually and adaptively in dynamic environments. A new continual learning fault diagnosis method (CLFD) is proposed in this paper to solve a series of fault diagnosis tasks with machine increments. The stability–plasticity dilemma is an intrinsic issue in continual learning. The core of CLFD is the proposed dual-branch adaptive aggregation residual network (DAARN). Two types of residual blocks are created in each block layer of DAARN: steady and dynamic blocks. The stability–plasticity dilemma is solved by assigning them with adaptive aggregation weights to balance stability and plasticity, and a bi-level optimization program is used to optimize adaptive aggregation weights and model parameters. In addition, a feature-level knowledge distillation loss function is proposed to further overcome catastrophic forgetting. CLFD is then applied to the fault diagnosis case with machine increments. Results demonstrate that CLFD outperforms other continual learning methods and has satisfactory robustness.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.cja.2022.08.019
- OA Status
- hybrid
- Cited By
- 42
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293004936
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4293004936Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.cja.2022.08.019Digital Object Identifier
- Title
-
Continual learning fault diagnosis: A dual-branch adaptive aggregation residual network for fault diagnosis with machine incrementsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-24Full publication date if available
- Authors
-
Bojian Chen, Changqing Shen, Juanjuan Shi, Lin Kong, Luyang Tan, Dong Wang, Zhongkui ZhuList of authors in order
- Landing page
-
https://doi.org/10.1016/j.cja.2022.08.019Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.cja.2022.08.019Direct OA link when available
- Concepts
-
Fault (geology), Computer science, Forgetting, Residual, Artificial intelligence, Stability (learning theory), Machine learning, Algorithm, Seismology, Geology, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
42Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 21, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4293004936 |
|---|---|
| doi | https://doi.org/10.1016/j.cja.2022.08.019 |
| ids.doi | https://doi.org/10.1016/j.cja.2022.08.019 |
| ids.openalex | https://openalex.org/W4293004936 |
| fwci | 6.26966983 |
| type | article |
| title | Continual learning fault diagnosis: A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments |
| awards[0].id | https://openalex.org/G4579352373 |
| awards[0].funder_id | https://openalex.org/F4320321543 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2021M701503 |
| awards[0].funder_display_name | China Postdoctoral Science Foundation |
| awards[1].id | https://openalex.org/G5163084661 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 52272440 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| awards[2].id | https://openalex.org/G3635988892 |
| awards[2].funder_id | https://openalex.org/F4320321001 |
| awards[2].display_name | |
| awards[2].funder_award_id | 51875375 |
| awards[2].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 6 |
| biblio.volume | 36 |
| biblio.last_page | 377 |
| biblio.first_page | 361 |
| topics[0].id | https://openalex.org/T10220 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.994700014591217 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2207 |
| topics[0].subfield.display_name | Control and Systems Engineering |
| topics[0].display_name | Machine Fault Diagnosis Techniques |
| topics[1].id | https://openalex.org/T11307 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9878000020980835 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Domain Adaptation and Few-Shot Learning |
| topics[2].id | https://openalex.org/T12169 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9796000123023987 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2210 |
| topics[2].subfield.display_name | Mechanical Engineering |
| topics[2].display_name | Non-Destructive Testing Techniques |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320321543 |
| funders[1].ror | https://ror.org/0426zh255 |
| funders[1].display_name | China Postdoctoral Science Foundation |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | USD |
| apc_list.value_usd | 2000 |
| apc_paid.value | 2000 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2000 |
| concepts[0].id | https://openalex.org/C175551986 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6707994937896729 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q47089 |
| concepts[0].display_name | Fault (geology) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.598328709602356 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C7149132 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5671098828315735 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1377840 |
| concepts[2].display_name | Forgetting |
| concepts[3].id | https://openalex.org/C155512373 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5364127159118652 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q287450 |
| concepts[3].display_name | Residual |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4874127209186554 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C112972136 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4833661913871765 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7595718 |
| concepts[5].display_name | Stability (learning theory) |
| concepts[6].id | https://openalex.org/C119857082 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4560645818710327 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[6].display_name | Machine learning |
| concepts[7].id | https://openalex.org/C11413529 |
| concepts[7].level | 1 |
| concepts[7].score | 0.2167363166809082 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[7].display_name | Algorithm |
| concepts[8].id | https://openalex.org/C165205528 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q83371 |
| concepts[8].display_name | Seismology |
| concepts[9].id | https://openalex.org/C127313418 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[9].display_name | Geology |
| concepts[10].id | https://openalex.org/C41895202 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[10].display_name | Linguistics |
| concepts[11].id | https://openalex.org/C138885662 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[11].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/fault |
| keywords[0].score | 0.6707994937896729 |
| keywords[0].display_name | Fault (geology) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.598328709602356 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/forgetting |
| keywords[2].score | 0.5671098828315735 |
| keywords[2].display_name | Forgetting |
| keywords[3].id | https://openalex.org/keywords/residual |
| keywords[3].score | 0.5364127159118652 |
| keywords[3].display_name | Residual |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.4874127209186554 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/stability |
| keywords[5].score | 0.4833661913871765 |
| keywords[5].display_name | Stability (learning theory) |
| keywords[6].id | https://openalex.org/keywords/machine-learning |
| keywords[6].score | 0.4560645818710327 |
| keywords[6].display_name | Machine learning |
| keywords[7].id | https://openalex.org/keywords/algorithm |
| keywords[7].score | 0.2167363166809082 |
| keywords[7].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.1016/j.cja.2022.08.019 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S42274841 |
| locations[0].source.issn | 1000-9361, 2588-9230 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1000-9361 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Chinese Journal of Aeronautics |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Chinese Journal of Aeronautics |
| locations[0].landing_page_url | https://doi.org/10.1016/j.cja.2022.08.019 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5101724769 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9829-0856 |
| authorships[0].author.display_name | Bojian Chen |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Rail Transportation, Soochow University, Suzhou 215131, China |
| authorships[0].institutions[0].id | https://openalex.org/I3923682 |
| authorships[0].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Soochow University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Bojian CHEN |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Rail Transportation, Soochow University, Suzhou 215131, China |
| authorships[1].author.id | https://openalex.org/A5091072755 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5143-8366 |
| authorships[1].author.display_name | Changqing Shen |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Rail Transportation, Soochow University, Suzhou 215131, China |
| authorships[1].institutions[0].id | https://openalex.org/I3923682 |
| authorships[1].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Soochow University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Changqing SHEN |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | School of Rail Transportation, Soochow University, Suzhou 215131, China |
| authorships[2].author.id | https://openalex.org/A5043608454 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8634-9083 |
| authorships[2].author.display_name | Juanjuan Shi |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Rail Transportation, Soochow University, Suzhou 215131, China |
| authorships[2].institutions[0].id | https://openalex.org/I3923682 |
| authorships[2].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Soochow University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Juanjuan SHI |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Rail Transportation, Soochow University, Suzhou 215131, China |
| authorships[3].author.id | https://openalex.org/A5035001523 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-3737-1326 |
| authorships[3].author.display_name | Lin Kong |
| authorships[3].affiliations[0].raw_affiliation_string | China Chang Guang Satellite Technology CO.LTD, Changchun 130102, China |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Lin KONG |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | China Chang Guang Satellite Technology CO.LTD, Changchun 130102, China |
| authorships[4].author.id | https://openalex.org/A5112472318 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Luyang Tan |
| authorships[4].affiliations[0].raw_affiliation_string | China Chang Guang Satellite Technology CO.LTD, Changchun 130102, China |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Luyang TAN |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | China Chang Guang Satellite Technology CO.LTD, Changchun 130102, China |
| authorships[5].author.id | https://openalex.org/A5100391562 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-4872-4860 |
| authorships[5].author.display_name | Dong Wang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I183067930 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Industrial Engineering and Management and in the State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200030, China |
| authorships[5].institutions[0].id | https://openalex.org/I183067930 |
| authorships[5].institutions[0].ror | https://ror.org/0220qvk04 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I183067930 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Shanghai Jiao Tong University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Dong WANG |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Industrial Engineering and Management and in the State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200030, China |
| authorships[6].author.id | https://openalex.org/A5100758788 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-9827-4154 |
| authorships[6].author.display_name | Zhongkui Zhu |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Rail Transportation, Soochow University, Suzhou 215131, China |
| authorships[6].institutions[0].id | https://openalex.org/I3923682 |
| authorships[6].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Soochow University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Zhongkui ZHU |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | School of Rail Transportation, Soochow University, Suzhou 215131, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.cja.2022.08.019 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Continual learning fault diagnosis: A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10220 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.994700014591217 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2207 |
| primary_topic.subfield.display_name | Control and Systems Engineering |
| primary_topic.display_name | Machine Fault Diagnosis Techniques |
| related_works | https://openalex.org/W4289718052, https://openalex.org/W2164121020, https://openalex.org/W2145559838, https://openalex.org/W2905319430, https://openalex.org/W3116498279, https://openalex.org/W4287549553, https://openalex.org/W4310285384, https://openalex.org/W3183027292, https://openalex.org/W4248896073, https://openalex.org/W2974871044 |
| cited_by_count | 42 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 16 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 21 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 4 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1016/j.cja.2022.08.019 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S42274841 |
| best_oa_location.source.issn | 1000-9361, 2588-9230 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1000-9361 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Chinese Journal of Aeronautics |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Chinese Journal of Aeronautics |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.cja.2022.08.019 |
| primary_location.id | doi:10.1016/j.cja.2022.08.019 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S42274841 |
| primary_location.source.issn | 1000-9361, 2588-9230 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1000-9361 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Chinese Journal of Aeronautics |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Chinese Journal of Aeronautics |
| primary_location.landing_page_url | https://doi.org/10.1016/j.cja.2022.08.019 |
| publication_date | 2022-08-24 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3213908205, https://openalex.org/W6811108901, https://openalex.org/W6784768062, https://openalex.org/W3009370740, https://openalex.org/W6811168968, https://openalex.org/W3209651137, https://openalex.org/W1682403713, https://openalex.org/W3128553897, https://openalex.org/W3030364939, https://openalex.org/W3097816393, https://openalex.org/W2887782657, https://openalex.org/W3126725177, https://openalex.org/W3137410503, https://openalex.org/W6730146409, https://openalex.org/W6762993283, https://openalex.org/W2037979274, https://openalex.org/W6687483927, https://openalex.org/W2560647685, https://openalex.org/W2473930607, https://openalex.org/W6757205449, https://openalex.org/W6781568746, https://openalex.org/W6785582252, https://openalex.org/W6769359475, https://openalex.org/W6849896277, https://openalex.org/W6775714551, https://openalex.org/W6762867720, https://openalex.org/W6649927145, https://openalex.org/W6756810460, https://openalex.org/W6750782335, https://openalex.org/W4226468972, https://openalex.org/W2964189064, https://openalex.org/W2972313371, https://openalex.org/W2798836702, https://openalex.org/W3034867292, https://openalex.org/W4225842653, https://openalex.org/W3100156920, https://openalex.org/W2194775991, https://openalex.org/W3093638387, https://openalex.org/W2954929116, https://openalex.org/W2981864462, https://openalex.org/W2948734064, https://openalex.org/W2963943197, https://openalex.org/W3107810305, https://openalex.org/W2522381472 |
| referenced_works_count | 44 |
| abstract_inverted_index.A | 25, 129 |
| abstract_inverted_index.a | 1, 39, 47, 144, 211, 227 |
| abstract_inverted_index.As | 0 |
| abstract_inverted_index.In | 225 |
| abstract_inverted_index.an | 157 |
| abstract_inverted_index.by | 79, 198 |
| abstract_inverted_index.in | 32, 46, 60, 74, 126, 139, 160, 183 |
| abstract_inverted_index.is | 76, 114, 137, 156, 167, 196, 215, 233, 241 |
| abstract_inverted_index.of | 38, 50, 82, 92, 99, 105, 146, 165, 178, 187 |
| abstract_inverted_index.on | 17 |
| abstract_inverted_index.to | 11, 21, 96, 110, 142, 205, 217, 235, 244 |
| abstract_inverted_index.DL, | 83 |
| abstract_inverted_index.The | 153, 163, 193 |
| abstract_inverted_index.Two | 176 |
| abstract_inverted_index.all | 107 |
| abstract_inverted_index.and | 54, 124, 190, 208, 210, 222, 261 |
| abstract_inverted_index.are | 44, 64, 181 |
| abstract_inverted_index.has | 262 |
| abstract_inverted_index.may | 28 |
| abstract_inverted_index.new | 55, 89, 93, 130 |
| abstract_inverted_index.old | 100 |
| abstract_inverted_index.the | 13, 33, 80, 103, 112, 168, 245 |
| abstract_inverted_index.CLFD | 166, 240, 255 |
| abstract_inverted_index.also | 65 |
| abstract_inverted_index.case | 248 |
| abstract_inverted_index.core | 164 |
| abstract_inverted_index.cost | 104 |
| abstract_inverted_index.data | 16, 43, 73, 91, 109 |
| abstract_inverted_index.deep | 4 |
| abstract_inverted_index.each | 184 |
| abstract_inverted_index.flow | 49 |
| abstract_inverted_index.loss | 231 |
| abstract_inverted_index.need | 10 |
| abstract_inverted_index.that | 254 |
| abstract_inverted_index.them | 200 |
| abstract_inverted_index.then | 242 |
| abstract_inverted_index.this | 140 |
| abstract_inverted_index.used | 216 |
| abstract_inverted_index.will | 57 |
| abstract_inverted_index.with | 88, 150, 201, 249 |
| abstract_inverted_index.block | 185 |
| abstract_inverted_index.fault | 7, 19, 42, 72, 90, 118, 133, 147, 246 |
| abstract_inverted_index.high. | 116 |
| abstract_inverted_index.issue | 159 |
| abstract_inverted_index.known | 108 |
| abstract_inverted_index.layer | 186 |
| abstract_inverted_index.leads | 95 |
| abstract_inverted_index.learn | 122 |
| abstract_inverted_index.model | 223 |
| abstract_inverted_index.occur | 59 |
| abstract_inverted_index.other | 257 |
| abstract_inverted_index.paper | 141 |
| abstract_inverted_index.solve | 143 |
| abstract_inverted_index.tasks | 149 |
| abstract_inverted_index.types | 20, 177 |
| abstract_inverted_index.which | 63 |
| abstract_inverted_index.while | 102 |
| abstract_inverted_index.(CLFD) | 136 |
| abstract_inverted_index.DAARN: | 188 |
| abstract_inverted_index.During | 35 |
| abstract_inverted_index.blocks | 180 |
| abstract_inverted_index.called | 66 |
| abstract_inverted_index.cannot | 121 |
| abstract_inverted_index.faults | 56 |
| abstract_inverted_index.method | 135 |
| abstract_inverted_index.models | 86, 113 |
| abstract_inverted_index.series | 145 |
| abstract_inverted_index.solved | 197 |
| abstract_inverted_index.steady | 189 |
| abstract_inverted_index.system | 27 |
| abstract_inverted_index.tasks, | 101 |
| abstract_inverted_index.Limited | 78 |
| abstract_inverted_index.Results | 252 |
| abstract_inverted_index.achieve | 22 |
| abstract_inverted_index.advance | 75 |
| abstract_inverted_index.applied | 243 |
| abstract_inverted_index.balance | 206 |
| abstract_inverted_index.blocks. | 192 |
| abstract_inverted_index.collect | 12 |
| abstract_inverted_index.created | 182 |
| abstract_inverted_index.dilemma | 155, 195 |
| abstract_inverted_index.dynamic | 127, 191 |
| abstract_inverted_index.further | 236 |
| abstract_inverted_index.include | 29 |
| abstract_inverted_index.machine | 18, 67, 151, 250 |
| abstract_inverted_index.methods | 9, 120, 260 |
| abstract_inverted_index.network | 174 |
| abstract_inverted_index.program | 214 |
| abstract_inverted_index.retrain | 111 |
| abstract_inverted_index.system, | 41 |
| abstract_inverted_index.weights | 204, 221 |
| abstract_inverted_index.(DAARN). | 175 |
| abstract_inverted_index.DL-based | 117 |
| abstract_inverted_index.adaptive | 171, 202, 219 |
| abstract_inverted_index.bi-level | 212 |
| abstract_inverted_index.directly | 87 |
| abstract_inverted_index.existing | 85 |
| abstract_inverted_index.function | 232 |
| abstract_inverted_index.learning | 5, 132, 259 |
| abstract_inverted_index.multiple | 30 |
| abstract_inverted_index.optimize | 218 |
| abstract_inverted_index.overcome | 237 |
| abstract_inverted_index.proposed | 138, 169, 234 |
| abstract_inverted_index.residual | 173, 179 |
| abstract_inverted_index.training | 84 |
| abstract_inverted_index.addition, | 226 |
| abstract_inverted_index.approach, | 3 |
| abstract_inverted_index.assigning | 199 |
| abstract_inverted_index.condition | 36 |
| abstract_inverted_index.continual | 131, 161, 258 |
| abstract_inverted_index.diagnosis | 8, 119, 134, 148, 247 |
| abstract_inverted_index.generated | 52 |
| abstract_inverted_index.intrinsic | 158 |
| abstract_inverted_index.knowledge | 229 |
| abstract_inverted_index.learning. | 162 |
| abstract_inverted_index.stability | 207 |
| abstract_inverted_index.(DL)-based | 6 |
| abstract_inverted_index.Therefore, | 69 |
| abstract_inverted_index.adaptively | 125 |
| abstract_inverted_index.adequately | 70 |
| abstract_inverted_index.collecting | 71, 106 |
| abstract_inverted_index.constantly | 51 |
| abstract_inverted_index.continuous | 48 |
| abstract_inverted_index.difficult. | 77 |
| abstract_inverted_index.forgetting | 98 |
| abstract_inverted_index.inevitably | 58 |
| abstract_inverted_index.mechanical | 26, 40 |
| abstract_inverted_index.monitoring | 37 |
| abstract_inverted_index.relatively | 14 |
| abstract_inverted_index.aggregation | 172, 203, 220 |
| abstract_inverted_index.continually | 123 |
| abstract_inverted_index.data-driven | 2 |
| abstract_inverted_index.demonstrate | 253 |
| abstract_inverted_index.distributed | 45 |
| abstract_inverted_index.dual-branch | 170 |
| abstract_inverted_index.excessively | 115 |
| abstract_inverted_index.forgetting. | 239 |
| abstract_inverted_index.increments. | 68, 152, 251 |
| abstract_inverted_index.information | 53 |
| abstract_inverted_index.outperforms | 256 |
| abstract_inverted_index.parameters. | 224 |
| abstract_inverted_index.plasticity, | 209 |
| abstract_inverted_index.real-world. | 34 |
| abstract_inverted_index.robustness. | 264 |
| abstract_inverted_index.submachines | 31, 94 |
| abstract_inverted_index.catastrophic | 97, 238 |
| abstract_inverted_index.distillation | 230 |
| abstract_inverted_index.optimization | 213 |
| abstract_inverted_index.performance. | 24 |
| abstract_inverted_index.satisfactory | 23, 263 |
| abstract_inverted_index.submachines, | 62 |
| abstract_inverted_index.unconsidered | 61 |
| abstract_inverted_index.comprehensive | 15 |
| abstract_inverted_index.environments. | 128 |
| abstract_inverted_index.feature-level | 228 |
| abstract_inverted_index.characteristics | 81 |
| abstract_inverted_index.stability–plasticity | 154, 194 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5091072755 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I3923682 |
| citation_normalized_percentile.value | 0.96498529 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |