Generative autoencoder to prevent overregularization of variational autoencoder
In machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low‐dimensional posterior distribution given high‐dimensional data. Specifically, it optim...
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| Veröffentlicht in: | ETRI journal Jg. 47; H. 1; S. 80 - 89 |
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Electronics and Telecommunications Research Institute (ETRI)
01.02.2025
한국전자통신연구원 |
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| Abstract | In machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low‐dimensional posterior distribution given high‐dimensional data. Specifically, it optimizes the evidence lower bound from regularization and reconstruction terms, but the two terms are imbalanced in general. If the reconstruction error is not sufficiently small to belong to the population, the generative model performance cannot be guaranteed. We propose a generative autoencoder (GAE) that uses an autoencoder to first minimize the reconstruction error and then estimate the distribution using latent vectors mapped onto a lower dimension through the encoder. We compare the Fréchet inception distances scores of the proposed GAE and nine other variational autoencoders on the MNIST, Fashion MNIST, CIFAR10, and SVHN datasets. The proposed GAE consistently outperforms the other methods on the MNIST (44.30), Fashion MNIST (196.34), and SVHN (77.53) datasets. |
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| AbstractList | In machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low‐dimensional posterior distribution given high‐dimensional data. Specifically, it optimizes the evidence lower bound from regularization and reconstruction terms, but the two terms are imbalanced in general. If the reconstruction error is not sufficiently small to belong to the population, the generative model performance cannot be guaranteed. We propose a generative autoencoder (GAE) that uses an autoencoder to first minimize the reconstruction error and then estimate the distribution using latent vectors mapped onto a lower dimension through the encoder. We compare the Fréchet inception distances scores of the proposed GAE and nine other variational autoencoders on the MNIST, Fashion MNIST, CIFAR10, and SVHN datasets. The proposed GAE consistently outperforms the other methods on the MNIST (44.30), Fashion MNIST (196.34), and SVHN (77.53) datasets. In machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low-dimensional posterior dis-tribution given high-dimensional data. Specifically, it optimizes the evidence lower bound from regularization and reconstruction terms, but the two terms are imbalanced in general. If the reconstruction error is not sufficiently small to belong to the population, the generative model performance cannot be guaran-teed. We propose a generative autoencoder (GAE) that uses an autoencoder to first minimize the reconstruction error and then estimate the distribution using latent vectors mapped onto a lower dimension through the encoder. We com-pare the Fréchet inception distances scores of the proposed GAE and nine other variational autoencoders on the MNIST, Fashion MNIST, CIFAR10, and SVHN datasets. The proposed GAE consistently outperforms the other methods on the MNIST (44.30), Fashion MNIST (196.34), and SVHN (77.53) datasets. In machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low-dimensional posterior dis-tribution given high-dimensional data. Specifically, it optimizes the evidence lower bound from regularization and reconstruction terms, but the two terms are imbalanced in general. If the reconstruction error is not sufficiently small to belong to the population, the generative model performance cannot be guaran-teed. We propose a generative autoencoder (GAE) that uses an autoencoder to first minimize the reconstruction error and then estimate the distribution using latent vectors mapped onto a lower dimension through the encoder. We com-pare the Fréchet inception distances scores of the proposed GAE and nine other variational autoencoders on the MNIST, Fashion MNIST, CIFAR10, and SVHN datasets. The proposed GAE consistently outperforms the other methods on the MNIST (44.30), Fashion MNIST (196.34), and SVHN (77.53) datasets. KCI Citation Count: 0 |
| Author | Kim, YoungSoo Ko, YoungMin Ko, SunWoo |
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| Cites_doi | 10.48550/arXiv.1606.05908 10.1109/TPAMI.1979.4766926 10.1561/9781680836233 10.48550/arXiv.1701.00160 10.1109/CVPR.2019.01052 10.1007/s10994-019-05791-5 10.24963/ijcai.2017/273 10.1007/978-0-387-45528-0 10.48550/arXiv.1305.1707 10.1002/gamm.202100008 10.48550/arXiv.1611.02731 10.1109/ACCESS.2020.2977671 10.1126/science.290.5500.2323 10.1109/TPAMI.2013.57 10.48550/arXiv.1903.12436 10.48550/arXiv.1903.05789 10.1109/TIP.2020.2964429 10.48550/arXiv.1611.02648 10.1109/TKDE.2006.17 10.48550/arXiv.1312.6114 10.1111/jawr.12182 10.1609/aaai.v33i01.33015885 10.48550/arXiv.1804.00891 10.48550/arXiv.1711.01558 10.1109/TIT.1968.1054102 |
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| Notes | Funding information This study was supported by a research grant of Jeonju University in 2022. https://doi.org/10.4218/etrij.2023-0375 |
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| Title | Generative autoencoder to prevent overregularization of variational autoencoder |
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