Distance Guided Generative Adversarial Network for Explainable Binary Classifications Article Swipe
Xiangyu Xiong
,
Yue Sun
,
Xiaohong Liu
,
Wei Ke
,
Chan–Tong Lam
,
Jiangang Chen
,
Mingfeng Jiang
,
Mingwei Wang
,
Hui Xie
,
Tong Tong
,
Qinquan Gao
,
Hao Chen
,
Tao Tan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.17538
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.17538
Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-domain samples with limited variety. These previous methods make limited contributions to describing the decision boundaries for binary classification. In this paper, we propose a distance guided GAN (DisGAN) which controls the variation degrees of generated samples in the hyperplane space. Specifically, we instantiate the idea of DisGAN by combining two ways. The first way is vertical distance GAN (VerDisGAN) where the inter-domain generation is conditioned on the vertical distances. The second way is horizontal distance GAN (HorDisGAN) where the intra-domain generation is conditioned on the horizontal distances. Furthermore, VerDisGAN can produce the class-specific regions by mapping the source images to the hyperplane. Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification. The proposed method can apply to different classification architectures and has potential to extend to multi-class classification.
Related Topics
Concepts
Hyperplane
Binary number
Domain (mathematical analysis)
Computer science
Adversarial system
Class (philosophy)
Generative grammar
Generative adversarial network
Binary classification
Artificial intelligence
Pattern recognition (psychology)
Variation (astronomy)
Variety (cybernetics)
Algorithm
Machine learning
Mathematics
Image (mathematics)
Support vector machine
Combinatorics
Mathematical analysis
Physics
Arithmetic
Astrophysics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.17538
- https://arxiv.org/pdf/2312.17538
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390489797
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390489797Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2312.17538Digital Object Identifier
- Title
-
Distance Guided Generative Adversarial Network for Explainable Binary ClassificationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-29Full publication date if available
- Authors
-
Xiangyu Xiong, Yue Sun, Xiaohong Liu, Wei Ke, Chan–Tong Lam, Jiangang Chen, Mingfeng Jiang, Mingwei Wang, Hui Xie, Tong Tong, Qinquan Gao, Hao Chen, Tao TanList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.17538Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.17538Direct 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/2312.17538Direct OA link when available
- Concepts
-
Hyperplane, Binary number, Domain (mathematical analysis), Computer science, Adversarial system, Class (philosophy), Generative grammar, Generative adversarial network, Binary classification, Artificial intelligence, Pattern recognition (psychology), Variation (astronomy), Variety (cybernetics), Algorithm, Machine learning, Mathematics, Image (mathematics), Support vector machine, Combinatorics, Mathematical analysis, Physics, Arithmetic, AstrophysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| 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/2312.17538 |
| 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/2312.17538 |
| primary_location.id | pmh:oai:arXiv.org:2312.17538 |
| 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/2312.17538 |
| 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/2312.17538 |
| publication_date | 2023-12-29 |
| publication_year | 2023 |
| referenced_works_count | 0 |
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| abstract_inverted_index.In | 51 |
| abstract_inverted_index.On | 22 |
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| abstract_inverted_index.we | 54, 74 |
| abstract_inverted_index.GAN | 59, 90, 108 |
| abstract_inverted_index.The | 84, 102, 150 |
| abstract_inverted_index.and | 159 |
| abstract_inverted_index.can | 122, 153 |
| abstract_inverted_index.for | 7, 48 |
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| abstract_inverted_index.source | 130 |
| abstract_inverted_index.space. | 72 |
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| abstract_inverted_index.mapping | 128 |
| abstract_inverted_index.methods | 14, 39, 145 |
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| abstract_inverted_index.propose | 55 |
| abstract_inverted_index.regions | 126 |
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| abstract_inverted_index.benefits | 3 |
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| abstract_inverted_index.networks | 29 |
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| abstract_inverted_index.variety. | 36 |
| abstract_inverted_index.vertical | 88, 100 |
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| abstract_inverted_index.VerDisGAN | 121 |
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| abstract_inverted_index.potential | 2, 161 |
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| abstract_inverted_index.generation | 95, 113 |
| abstract_inverted_index.generative | 27 |
| abstract_inverted_index.horizontal | 106, 118 |
| abstract_inverted_index.hyperplane | 71 |
| abstract_inverted_index.knowledge. | 21 |
| abstract_inverted_index.mitigating | 8 |
| abstract_inverted_index.(HorDisGAN) | 109 |
| abstract_inverted_index.(VerDisGAN) | 91 |
| abstract_inverted_index.adversarial | 28 |
| abstract_inverted_index.conditioned | 97, 115 |
| abstract_inverted_index.explainable | 147 |
| abstract_inverted_index.hyperplane. | 134 |
| abstract_inverted_index.instantiate | 75 |
| abstract_inverted_index.multi-class | 165 |
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| abstract_inverted_index.Experimental | 135 |
| abstract_inverted_index.Furthermore, | 120 |
| abstract_inverted_index.augmentation | 6, 13, 144 |
| abstract_inverted_index.consistently | 140 |
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| abstract_inverted_index.intra-domain | 20, 112 |
| abstract_inverted_index.Specifically, | 73 |
| abstract_inverted_index.architectures | 158 |
| abstract_inverted_index.contributions | 42 |
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| abstract_inverted_index.classification | 157 |
| abstract_inverted_index.insufficiency, | 11 |
| abstract_inverted_index.classification. | 50, 149, 166 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |