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• W3201127938 abstract "We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals. The compressed signal is represented as a latent vector and fed into a generator network that is trained to produce high quality realistic signals that minimize a target objective function. To efficiently quantize the compressed signal, non-uniformly quantized optimal latent vectors are identified by iterative back-propagation with alternating direction method of multipliers (ADMM) optimization performed for each iteration. The performance of the proposed signal compression method is assessed using multiple metrics including PSNR and MS-SSIM for image compression and also PESR, Kaldi, LSTM, and MLP performance for speech compression. Test results show that the proposed work outperforms recent state-of-the-art hand-crafted and deep learning-based signal compression methods." @default.
• W3201127938 created "2021-09-27" @default.
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• W3201127938 date "2021-09-23" @default.
• W3201127938 modified "2023-09-24" @default.
• W3201127938 title "Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization" @default.
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• W3201127938 doi "https://doi.org/10.48550/arxiv.2109.11168" @default.
• W3201127938 hasPublicationYear "2021" @default.
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