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- W3193853809 abstract "In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space. Recently, Operational Neural Networks (ONNs) that learn the best non-linearity from a set of alternatives, and their “self-organized” variants, Self-ONNs, that approximate any non-linearity via Taylor series have been proposed to address the limitations of convolutional layers and a fixed nonlinear activation. In this paper, we propose to replace the convolutional and GDN layers in the variational autoencoder with self-organized operational layers, and propose a novel self-organized variational autoencoder (Self-VAE) architecture that benefits from stronger non-linearity. The experimental results demonstrate that the proposed Self-VAE yields improvements in both rate-distortion performance and perceptual image quality." @default.
- W3193853809 created "2021-08-30" @default.
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- W3193853809 date "2021-09-19" @default.
- W3193853809 modified "2023-10-11" @default.
- W3193853809 title "Self-Organized Variational Autoencoders (Self-Vae) For Learned Image Compression" @default.
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- W3193853809 doi "https://doi.org/10.1109/icip42928.2021.9506041" @default.
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