Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection Article Swipe
Jing Zhang
,
Jianwen Xie
,
Nick Barnes
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.12211
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.12211
In this paper, we propose a noise-aware encoder-decoder framework to disentangle a clean saliency predictor from noisy training examples, where the noisy labels are generated by unsupervised handcrafted feature-based methods. The proposed model consists of two sub-models parameterized by neural networks: (1) a saliency predictor that maps input images to clean saliency maps, and (2) a noise generator, which is a latent variable model that produces noises from Gaussian latent vectors. The whole model that represents noisy labels is a sum of the two sub-models. The goal of training the model is to estimate the parameters of both sub-models, and simultaneously infer the corresponding latent vector of each noisy label. We propose to train the model by using an alternating back-propagation (ABP) algorithm, which alternates the following two steps: (1) learning back-propagation for estimating the parameters of two sub-models by gradient ascent, and (2) inferential back-propagation for inferring the latent vectors of training noisy examples by Langevin Dynamics. To prevent the network from converging to trivial solutions, we utilize an edge-aware smoothness loss to regularize hidden saliency maps to have similar structures as their corresponding images. Experimental results on several benchmark datasets indicate the effectiveness of the proposed model.
Related Topics
Concepts
Computer science
Noise (video)
Artificial intelligence
Benchmark (surveying)
Latent variable
Pattern recognition (psychology)
Encoder
Smoothness
Generator (circuit theory)
Parameterized complexity
Gaussian noise
Algorithm
Image (mathematics)
Mathematics
Physics
Mathematical analysis
Quantum mechanics
Operating system
Geodesy
Geography
Power (physics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.12211
- https://arxiv.org/pdf/2007.12211
- OA Status
- green
- Cited By
- 6
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3044805788
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3044805788Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2007.12211Digital Object Identifier
- Title
-
Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-23Full publication date if available
- Authors
-
Jing Zhang, Jianwen Xie, Nick BarnesList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.12211Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2007.12211Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2007.12211Direct OA link when available
- Concepts
-
Computer science, Noise (video), Artificial intelligence, Benchmark (surveying), Latent variable, Pattern recognition (psychology), Encoder, Smoothness, Generator (circuit theory), Parameterized complexity, Gaussian noise, Algorithm, Image (mathematics), Mathematics, Physics, Mathematical analysis, Quantum mechanics, Operating system, Geodesy, Geography, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 2, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3044805788 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2007.12211 |
| ids.doi | https://doi.org/10.48550/arxiv.2007.12211 |
| ids.mag | 3044805788 |
| ids.openalex | https://openalex.org/W3044805788 |
| fwci | |
| type | preprint |
| title | Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11605 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9986000061035156 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Visual Attention and Saliency Detection |
| topics[1].id | https://openalex.org/T10036 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9975000023841858 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Advanced Neural Network Applications |
| topics[2].id | https://openalex.org/T12859 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9945999979972839 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1304 |
| topics[2].subfield.display_name | Biophysics |
| topics[2].display_name | Cell Image Analysis Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7181839942932129 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C99498987 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6183515191078186 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[1].display_name | Noise (video) |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.597582221031189 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C185798385 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5879084467887878 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[3].display_name | Benchmark (surveying) |
| concepts[4].id | https://openalex.org/C51167844 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5792191028594971 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q4422623 |
| concepts[4].display_name | Latent variable |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5451764464378357 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C118505674 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4966920018196106 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q42586063 |
| concepts[6].display_name | Encoder |
| concepts[7].id | https://openalex.org/C102634674 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4565344750881195 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q868473 |
| concepts[7].display_name | Smoothness |
| concepts[8].id | https://openalex.org/C2780992000 |
| concepts[8].level | 3 |
| concepts[8].score | 0.44557666778564453 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q17016113 |
| concepts[8].display_name | Generator (circuit theory) |
| concepts[9].id | https://openalex.org/C165464430 |
| concepts[9].level | 2 |
| concepts[9].score | 0.43509641289711 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1570441 |
| concepts[9].display_name | Parameterized complexity |
| concepts[10].id | https://openalex.org/C4199805 |
| concepts[10].level | 2 |
| concepts[10].score | 0.42180579900741577 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2725903 |
| concepts[10].display_name | Gaussian noise |
| concepts[11].id | https://openalex.org/C11413529 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3109632134437561 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[11].display_name | Algorithm |
| concepts[12].id | https://openalex.org/C115961682 |
| concepts[12].level | 2 |
| concepts[12].score | 0.2599124312400818 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[12].display_name | Image (mathematics) |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.18035241961479187 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C121332964 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[14].display_name | Physics |
| concepts[15].id | https://openalex.org/C134306372 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[15].display_name | Mathematical analysis |
| concepts[16].id | https://openalex.org/C62520636 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[16].display_name | Quantum mechanics |
| concepts[17].id | https://openalex.org/C111919701 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[17].display_name | Operating system |
| concepts[18].id | https://openalex.org/C13280743 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q131089 |
| concepts[18].display_name | Geodesy |
| concepts[19].id | https://openalex.org/C205649164 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[19].display_name | Geography |
| concepts[20].id | https://openalex.org/C163258240 |
| concepts[20].level | 2 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[20].display_name | Power (physics) |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7181839942932129 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/noise |
| keywords[1].score | 0.6183515191078186 |
| keywords[1].display_name | Noise (video) |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.597582221031189 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/benchmark |
| keywords[3].score | 0.5879084467887878 |
| keywords[3].display_name | Benchmark (surveying) |
| keywords[4].id | https://openalex.org/keywords/latent-variable |
| keywords[4].score | 0.5792191028594971 |
| keywords[4].display_name | Latent variable |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.5451764464378357 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/encoder |
| keywords[6].score | 0.4966920018196106 |
| keywords[6].display_name | Encoder |
| keywords[7].id | https://openalex.org/keywords/smoothness |
| keywords[7].score | 0.4565344750881195 |
| keywords[7].display_name | Smoothness |
| keywords[8].id | https://openalex.org/keywords/generator |
| keywords[8].score | 0.44557666778564453 |
| keywords[8].display_name | Generator (circuit theory) |
| keywords[9].id | https://openalex.org/keywords/parameterized-complexity |
| keywords[9].score | 0.43509641289711 |
| keywords[9].display_name | Parameterized complexity |
| keywords[10].id | https://openalex.org/keywords/gaussian-noise |
| keywords[10].score | 0.42180579900741577 |
| keywords[10].display_name | Gaussian noise |
| keywords[11].id | https://openalex.org/keywords/algorithm |
| keywords[11].score | 0.3109632134437561 |
| keywords[11].display_name | Algorithm |
| keywords[12].id | https://openalex.org/keywords/image |
| keywords[12].score | 0.2599124312400818 |
| keywords[12].display_name | Image (mathematics) |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.18035241961479187 |
| keywords[13].display_name | Mathematics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2007.12211 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2007.12211 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2007.12211 |
| locations[1].id | doi:10.48550/arxiv.2007.12211 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2007.12211 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5100630815 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8516-0913 |
| authorships[0].author.display_name | Jing Zhang |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I118347636 |
| authorships[0].affiliations[0].raw_affiliation_string | Australian National University |
| authorships[0].institutions[0].id | https://openalex.org/I118347636 |
| authorships[0].institutions[0].ror | https://ror.org/019wvm592 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I118347636 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | Australian National University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jing Zhang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Australian National University |
| authorships[1].author.id | https://openalex.org/A5102966958 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6818-7444 |
| authorships[1].author.display_name | Jianwen Xie |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210159958 |
| authorships[1].affiliations[0].raw_affiliation_string | Cognitive Computing Lab, Baidu Research, Sunnyvale, USA |
| authorships[1].institutions[0].id | https://openalex.org/I4210159958 |
| authorships[1].institutions[0].ror | https://ror.org/04s361q55 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210159958 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Cognitive Research (United States) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jianwen Xie |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Cognitive Computing Lab, Baidu Research, Sunnyvale, USA |
| authorships[2].author.id | https://openalex.org/A5072837153 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9343-9535 |
| authorships[2].author.display_name | Nick Barnes |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I118347636 |
| authorships[2].affiliations[0].raw_affiliation_string | Australian National University, Canberra,, Australia |
| authorships[2].institutions[0].id | https://openalex.org/I118347636 |
| authorships[2].institutions[0].ror | https://ror.org/019wvm592 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I118347636 |
| authorships[2].institutions[0].country_code | AU |
| authorships[2].institutions[0].display_name | Australian National University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Nick Barnes |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Australian National University, Canberra,, Australia |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2007.12211 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2020-07-29T00:00:00 |
| display_name | Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11605 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9986000061035156 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Visual Attention and Saliency Detection |
| related_works | https://openalex.org/W2051058708, https://openalex.org/W1494268238, https://openalex.org/W154868527, https://openalex.org/W1983207144, https://openalex.org/W2490706771, https://openalex.org/W2480116122, https://openalex.org/W4255576661, https://openalex.org/W1516574938, https://openalex.org/W2563912921, https://openalex.org/W2407611282 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 3 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2007.12211 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2007.12211 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2007.12211 |
| primary_location.id | pmh:oai:arXiv.org:2007.12211 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2007.12211 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2007.12211 |
| publication_date | 2020-07-23 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2777511827, https://openalex.org/W3104979525, https://openalex.org/W2087100776, https://openalex.org/W1894057436, https://openalex.org/W2798807298, https://openalex.org/W2948500402, https://openalex.org/W1991367009, https://openalex.org/W2962864875, https://openalex.org/W2990984982, https://openalex.org/W2963032190, https://openalex.org/W2795251047, https://openalex.org/W2740667773, https://openalex.org/W2047670868, https://openalex.org/W2188365844, https://openalex.org/W2798825526, https://openalex.org/W2962159375, https://openalex.org/W2963685207, https://openalex.org/W2979302305, https://openalex.org/W2963971375, https://openalex.org/W2963868681, https://openalex.org/W2961348656, https://openalex.org/W2987701848, https://openalex.org/W2798791651, https://openalex.org/W2962762541, https://openalex.org/W3137695714, https://openalex.org/W2964352379, https://openalex.org/W2752971446, https://openalex.org/W3035633116, https://openalex.org/W2998449272, https://openalex.org/W2948510860, https://openalex.org/W2948300571, https://openalex.org/W1514928307, https://openalex.org/W2963081269, https://openalex.org/W3009828227, https://openalex.org/W2962835968, https://openalex.org/W2744613561, https://openalex.org/W1959608418, https://openalex.org/W2895251968, https://openalex.org/W2866634454, https://openalex.org/W2965615945, https://openalex.org/W2963020481, https://openalex.org/W2983929074, https://openalex.org/W2962680827, https://openalex.org/W2963529609, https://openalex.org/W2963112696, https://openalex.org/W2478027467, https://openalex.org/W1522301498, https://openalex.org/W2967052791, https://openalex.org/W2963420686, https://openalex.org/W2194775991, https://openalex.org/W2002781701, https://openalex.org/W21025885, https://openalex.org/W2996884277, https://openalex.org/W2981510929, https://openalex.org/W3034453930, https://openalex.org/W2998708909, https://openalex.org/W2963136160 |
| referenced_works_count | 57 |
| abstract_inverted_index.a | 5, 11, 42, 55, 60, 79 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.To | 158 |
| abstract_inverted_index.We | 110 |
| abstract_inverted_index.an | 118, 169 |
| abstract_inverted_index.as | 182 |
| abstract_inverted_index.by | 25, 38, 116, 139, 155 |
| abstract_inverted_index.is | 59, 78, 91 |
| abstract_inverted_index.of | 34, 81, 87, 96, 106, 136, 151, 195 |
| abstract_inverted_index.on | 188 |
| abstract_inverted_index.to | 9, 49, 92, 112, 164, 173, 178 |
| abstract_inverted_index.we | 3, 167 |
| abstract_inverted_index.(1) | 41, 129 |
| abstract_inverted_index.(2) | 54, 143 |
| abstract_inverted_index.The | 30, 71, 85 |
| abstract_inverted_index.and | 53, 99, 142 |
| abstract_inverted_index.are | 23 |
| abstract_inverted_index.for | 132, 146 |
| abstract_inverted_index.sum | 80 |
| abstract_inverted_index.the | 20, 82, 89, 94, 102, 114, 125, 134, 148, 160, 193, 196 |
| abstract_inverted_index.two | 35, 83, 127, 137 |
| abstract_inverted_index.both | 97 |
| abstract_inverted_index.each | 107 |
| abstract_inverted_index.from | 15, 67, 162 |
| abstract_inverted_index.goal | 86 |
| abstract_inverted_index.have | 179 |
| abstract_inverted_index.loss | 172 |
| abstract_inverted_index.maps | 46, 177 |
| abstract_inverted_index.that | 45, 64, 74 |
| abstract_inverted_index.this | 1 |
| abstract_inverted_index.(ABP) | 121 |
| abstract_inverted_index.clean | 12, 50 |
| abstract_inverted_index.infer | 101 |
| abstract_inverted_index.input | 47 |
| abstract_inverted_index.maps, | 52 |
| abstract_inverted_index.model | 32, 63, 73, 90, 115 |
| abstract_inverted_index.noise | 56 |
| abstract_inverted_index.noisy | 16, 21, 76, 108, 153 |
| abstract_inverted_index.their | 183 |
| abstract_inverted_index.train | 113 |
| abstract_inverted_index.using | 117 |
| abstract_inverted_index.where | 19 |
| abstract_inverted_index.which | 58, 123 |
| abstract_inverted_index.whole | 72 |
| abstract_inverted_index.hidden | 175 |
| abstract_inverted_index.images | 48 |
| abstract_inverted_index.label. | 109 |
| abstract_inverted_index.labels | 22, 77 |
| abstract_inverted_index.latent | 61, 69, 104, 149 |
| abstract_inverted_index.model. | 198 |
| abstract_inverted_index.neural | 39 |
| abstract_inverted_index.noises | 66 |
| abstract_inverted_index.paper, | 2 |
| abstract_inverted_index.steps: | 128 |
| abstract_inverted_index.vector | 105 |
| abstract_inverted_index.ascent, | 141 |
| abstract_inverted_index.images. | 185 |
| abstract_inverted_index.network | 161 |
| abstract_inverted_index.prevent | 159 |
| abstract_inverted_index.propose | 4, 111 |
| abstract_inverted_index.results | 187 |
| abstract_inverted_index.several | 189 |
| abstract_inverted_index.similar | 180 |
| abstract_inverted_index.trivial | 165 |
| abstract_inverted_index.utilize | 168 |
| abstract_inverted_index.vectors | 150 |
| abstract_inverted_index.Gaussian | 68 |
| abstract_inverted_index.Langevin | 156 |
| abstract_inverted_index.consists | 33 |
| abstract_inverted_index.datasets | 191 |
| abstract_inverted_index.estimate | 93 |
| abstract_inverted_index.examples | 154 |
| abstract_inverted_index.gradient | 140 |
| abstract_inverted_index.indicate | 192 |
| abstract_inverted_index.learning | 130 |
| abstract_inverted_index.methods. | 29 |
| abstract_inverted_index.produces | 65 |
| abstract_inverted_index.proposed | 31, 197 |
| abstract_inverted_index.saliency | 13, 43, 51, 176 |
| abstract_inverted_index.training | 17, 88, 152 |
| abstract_inverted_index.variable | 62 |
| abstract_inverted_index.vectors. | 70 |
| abstract_inverted_index.Dynamics. | 157 |
| abstract_inverted_index.benchmark | 190 |
| abstract_inverted_index.examples, | 18 |
| abstract_inverted_index.following | 126 |
| abstract_inverted_index.framework | 8 |
| abstract_inverted_index.generated | 24 |
| abstract_inverted_index.inferring | 147 |
| abstract_inverted_index.networks: | 40 |
| abstract_inverted_index.predictor | 14, 44 |
| abstract_inverted_index.algorithm, | 122 |
| abstract_inverted_index.alternates | 124 |
| abstract_inverted_index.converging | 163 |
| abstract_inverted_index.edge-aware | 170 |
| abstract_inverted_index.estimating | 133 |
| abstract_inverted_index.generator, | 57 |
| abstract_inverted_index.parameters | 95, 135 |
| abstract_inverted_index.regularize | 174 |
| abstract_inverted_index.represents | 75 |
| abstract_inverted_index.smoothness | 171 |
| abstract_inverted_index.solutions, | 166 |
| abstract_inverted_index.structures | 181 |
| abstract_inverted_index.sub-models | 36, 138 |
| abstract_inverted_index.alternating | 119 |
| abstract_inverted_index.disentangle | 10 |
| abstract_inverted_index.handcrafted | 27 |
| abstract_inverted_index.inferential | 144 |
| abstract_inverted_index.noise-aware | 6 |
| abstract_inverted_index.sub-models, | 98 |
| abstract_inverted_index.sub-models. | 84 |
| abstract_inverted_index.Experimental | 186 |
| abstract_inverted_index.unsupervised | 26 |
| abstract_inverted_index.corresponding | 103, 184 |
| abstract_inverted_index.effectiveness | 194 |
| abstract_inverted_index.feature-based | 28 |
| abstract_inverted_index.parameterized | 37 |
| abstract_inverted_index.simultaneously | 100 |
| abstract_inverted_index.encoder-decoder | 7 |
| abstract_inverted_index.back-propagation | 120, 131, 145 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |