Image Generation and Recognition Technology Based on Attention Residual GAN Article Swipe
Huazhe Wang
,
Li Ma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3287854
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3287854
In accordance with the concept of game antagonism, Generative Adversarial Network (GAN) is a popular model in current image generation technology. However, GAN has problems such as unstable training and difficult convergence, which seriously affect the effectiveness of input feature extraction and image recognition. The study introduces residual network structure and self attention mechanism to calculate the weight parameters of features, and then guides image generation through image label information. The improved GAN model classifier is applied to image recognition. The final experimental data shows that the Fréchet Inception Distance (FID) values of the iGAN in facial expressions and behavioral actions are 77.68 and 176.84, respectively, which are closer to the distribution of real image data. In behavioral image recognition, the accuracy of the model is 96.8%, and the required time is 30 seconds. In facial expression recognition, the accuracy and recognition time of the model are 90.1% and 24 seconds, respectively. This indicates that it can generate high-quality images, has stronger feature extraction capabilities, and has higher recognition efficiency. This model provides a new technical reference for the further improvement of image processing technology, and has certain application potential and value.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3287854
- https://ieeexplore.ieee.org/ielx7/6287639/10005208/10156812.pdf
- OA Status
- gold
- Cited By
- 10
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381327795
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381327795Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2023.3287854Digital Object Identifier
- Title
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Image Generation and Recognition Technology Based on Attention Residual GANWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Huazhe Wang, Li MaList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2023.3287854Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/10005208/10156812.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/10005208/10156812.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Residual, Feature extraction, Pattern recognition (psychology), Classifier (UML), Facial recognition system, Image (mathematics), Computer vision, AlgorithmTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 7Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.seconds, | 150 |
| abstract_inverted_index.seconds. | 133 |
| abstract_inverted_index.stronger | 161 |
| abstract_inverted_index.training | 28 |
| abstract_inverted_index.unstable | 27 |
| abstract_inverted_index.Inception | 88 |
| abstract_inverted_index.attention | 52 |
| abstract_inverted_index.calculate | 55 |
| abstract_inverted_index.difficult | 30 |
| abstract_inverted_index.features, | 60 |
| abstract_inverted_index.indicates | 153 |
| abstract_inverted_index.mechanism | 53 |
| abstract_inverted_index.potential | 189 |
| abstract_inverted_index.reference | 176 |
| abstract_inverted_index.seriously | 33 |
| abstract_inverted_index.structure | 49 |
| abstract_inverted_index.technical | 175 |
| abstract_inverted_index.Generative | 8 |
| abstract_inverted_index.accordance | 1 |
| abstract_inverted_index.behavioral | 99, 117 |
| abstract_inverted_index.classifier | 74 |
| abstract_inverted_index.expression | 136 |
| abstract_inverted_index.extraction | 40, 163 |
| abstract_inverted_index.generation | 19, 65 |
| abstract_inverted_index.introduces | 46 |
| abstract_inverted_index.parameters | 58 |
| abstract_inverted_index.processing | 183 |
| abstract_inverted_index.Adversarial | 9 |
| abstract_inverted_index.antagonism, | 7 |
| abstract_inverted_index.application | 188 |
| abstract_inverted_index.efficiency. | 169 |
| abstract_inverted_index.expressions | 97 |
| abstract_inverted_index.improvement | 180 |
| abstract_inverted_index.recognition | 141, 168 |
| abstract_inverted_index.technology, | 184 |
| abstract_inverted_index.technology. | 20 |
| abstract_inverted_index.90.1% | 147 |
| abstract_inverted_index.convergence, | 31 |
| abstract_inverted_index.distribution | 111 |
| abstract_inverted_index.experimental | 82 |
| abstract_inverted_index.high-quality | 158 |
| abstract_inverted_index.information. | 69 |
| abstract_inverted_index.recognition, | 119, 137 |
| abstract_inverted_index.recognition. | 43, 79 |
| abstract_inverted_index.96.8%, | 126 |
| abstract_inverted_index.capabilities, | 164 |
| abstract_inverted_index.effectiveness | 36 |
| abstract_inverted_index.respectively, | 105 |
| abstract_inverted_index.respectively. | 151 |
| abstract_inverted_index.Fréchet | 87 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.8409789 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |