Quaternion Vector Quantized Variational Autoencoder
Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in generative tasks. However, these models still face limitations in handling complex image reconstruction, part...
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| Vydáno v: | IEEE signal processing letters Ročník 32; s. 151 - 155 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1070-9908, 1558-2361 |
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| Abstract | Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in generative tasks. However, these models still face limitations in handling complex image reconstruction, particularly in preserving high-quality details. Moreover, quaternion neural networks have shown unique advantages in handling multi-dimensional data, indicating that integrating quaternion approaches could potentially improve the performance of these autoencoders. To this end, we propose QVQ-VAE, a lightweight network in the quaternion domain that introduces a quaternion-based quantization layer and training strategy to improve reconstruction precision. By fully leveraging quaternion operations, QVQ-VAE reduces the number of model parameters, thereby lowering computational resource demands. Extensive evaluations on face and general object reconstruction tasks show that QVQ-VAE consistently outperforms existing methods while using significantly fewer parameters. |
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| AbstractList | Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in generative tasks. However, these models still face limitations in handling complex image reconstruction, particularly in preserving high-quality details. Moreover, quaternion neural networks have shown unique advantages in handling multi-dimensional data, indicating that integrating quaternion approaches could potentially improve the performance of these autoencoders. To this end, we propose QVQ-VAE, a lightweight network in the quaternion domain that introduces a quaternion-based quantization layer and training strategy to improve reconstruction precision. By fully leveraging quaternion operations, QVQ-VAE reduces the number of model parameters, thereby lowering computational resource demands. Extensive evaluations on face and general object reconstruction tasks show that QVQ-VAE consistently outperforms existing methods while using significantly fewer parameters. |
| Author | Zhang, Yang Luo, Hui Liu, Xin Sun, Jian |
| Author_xml | – sequence: 1 givenname: Hui orcidid: 0009-0005-6189-5260 surname: Luo fullname: Luo, Hui email: mogu_hl@163.com organization: Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China – sequence: 2 givenname: Xin orcidid: 0000-0001-9646-4448 surname: Liu fullname: Liu, Xin email: xiliu@must.edu.mo organization: Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China – sequence: 3 givenname: Jian orcidid: 0009-0006-6485-0751 surname: Sun fullname: Sun, Jian email: jiansun97@163.com organization: Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China – sequence: 4 givenname: Yang orcidid: 0000-0002-0540-0893 surname: Zhang fullname: Zhang, Yang email: yang.zhang@umanitoba.ca organization: Department of Mathematics, University of Manitoba, Winnipeg, MB, Canada |
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| SubjectTerms | Convolution Decoding Face recognition Image quality Image reconstruction Indexes Multidimensional data Neural networks Parameters Quantization (signal) quaternion generative models quaternion neural networks Quaternions Task complexity Training vector quantized variational autoencoder Vectors |
| Title | Quaternion Vector Quantized Variational Autoencoder |
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