Unsupervised learning of image data using generative adversarial network
Over the past few years, with the introduction of deep learning techniques such as convolution neural network (CNN), supervised learning with CNN had achieved a huge success in the computer vision area such as classifying digital images. However, supervised learning has a major drawback, in which it...
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| Format: | Proceedings |
| Language: | English English |
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Springer, Singapore
2020
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| Online Access: | https://eprints.ums.edu.my/id/eprint/27636/1/Unsupervised%20learning%20of%20image%20data%20using%20generative%20adversarial%20network-Abstract.pdf https://eprints.ums.edu.my/id/eprint/27636/2/Unsupervised%20learning%20of%20image%20data%20using%20generative%20adversarial%20network.pdf |
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| author | Rayner Alfred Lun,, Chew Ye |
| author_facet | Rayner Alfred Lun,, Chew Ye |
| author_sort | Rayner Alfred |
| collection | UMS |
| description | Over the past few years, with the introduction of deep learning techniques such as convolution neural network (CNN), supervised learning with CNN had achieved a huge success in the computer vision area such as classifying digital images. However, supervised learning has a major drawback, in which it requires a large dataset for them to perform more effectively. As the data used in training grew bigger, the cost of labeling data for training becomes more expensive and impractical. In order to resolve this issue, unsupervised learning is encouraged to be used as it can draw inferences from datasets consisting of unlabeled input data. Generative adversarial network (GAN) is one of the unsupervised learning technique that has the ability to create natural-looking images, converting text description into images, recover resolution of images and last but not least, its power of representation learning from unlabeled data. Thus, this study attempts to evaluate the effectiveness of GAN algorithm in performing the supervised task and unsupervised task such as classification and clustering. Based on the results obtained, the GAN algorithm can learn the internal representation of data without labels and can act as good features extractor. Future works include applying GAN framework in other domains such as video, natural language processing and text to image synthesis. |
| first_indexed | 2024-03-06T03:08:08Z |
| format | Proceedings |
| id | ums.eprints-27636 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:08:08Z |
| publishDate | 2020 |
| publisher | Springer, Singapore |
| record_format | dspace |
| spelling | ums.eprints-276362021-07-07T01:34:23Z https://eprints.ums.edu.my/id/eprint/27636/ Unsupervised learning of image data using generative adversarial network Rayner Alfred Lun,, Chew Ye LB Theory and practice of education T Technology (General) Over the past few years, with the introduction of deep learning techniques such as convolution neural network (CNN), supervised learning with CNN had achieved a huge success in the computer vision area such as classifying digital images. However, supervised learning has a major drawback, in which it requires a large dataset for them to perform more effectively. As the data used in training grew bigger, the cost of labeling data for training becomes more expensive and impractical. In order to resolve this issue, unsupervised learning is encouraged to be used as it can draw inferences from datasets consisting of unlabeled input data. Generative adversarial network (GAN) is one of the unsupervised learning technique that has the ability to create natural-looking images, converting text description into images, recover resolution of images and last but not least, its power of representation learning from unlabeled data. Thus, this study attempts to evaluate the effectiveness of GAN algorithm in performing the supervised task and unsupervised task such as classification and clustering. Based on the results obtained, the GAN algorithm can learn the internal representation of data without labels and can act as good features extractor. Future works include applying GAN framework in other domains such as video, natural language processing and text to image synthesis. Springer, Singapore 2020 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/27636/1/Unsupervised%20learning%20of%20image%20data%20using%20generative%20adversarial%20network-Abstract.pdf text en https://eprints.ums.edu.my/id/eprint/27636/2/Unsupervised%20learning%20of%20image%20data%20using%20generative%20adversarial%20network.pdf Rayner Alfred and Lun,, Chew Ye (2020) Unsupervised learning of image data using generative adversarial network. https://www.scopus.com/record/display.uri?eid=2-s2.0-85077110455&origin=inward&txGid=91854adc5da594e451d75d9e8b135132 |
| spellingShingle | LB Theory and practice of education T Technology (General) Rayner Alfred Lun,, Chew Ye Unsupervised learning of image data using generative adversarial network |
| title | Unsupervised learning of image data using generative adversarial network |
| title_full | Unsupervised learning of image data using generative adversarial network |
| title_fullStr | Unsupervised learning of image data using generative adversarial network |
| title_full_unstemmed | Unsupervised learning of image data using generative adversarial network |
| title_short | Unsupervised learning of image data using generative adversarial network |
| title_sort | unsupervised learning of image data using generative adversarial network |
| topic | LB Theory and practice of education T Technology (General) |
| url | https://eprints.ums.edu.my/id/eprint/27636/1/Unsupervised%20learning%20of%20image%20data%20using%20generative%20adversarial%20network-Abstract.pdf https://eprints.ums.edu.my/id/eprint/27636/2/Unsupervised%20learning%20of%20image%20data%20using%20generative%20adversarial%20network.pdf |
| work_keys_str_mv | AT rayneralfred unsupervisedlearningofimagedatausinggenerativeadversarialnetwork AT lunchewye unsupervisedlearningofimagedatausinggenerativeadversarialnetwork |