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UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS

Citation Author(s):
Submitted by:
Bowen Liu
Last updated:
16 May 2020 - 4:30pm
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Bowen Liu
Paper Code:
4405

Abstract 

Abstract: 

We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals. The compressed signal is represented by a latent vector fed into a generator network which is trained to produce high-quality 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 ADMM optimization performed for each iteration. Our experiments show that the proposed algorithm outperforms prior signal compression methods for both image and speech compression quantified in various metrics including bit rate, PSNR, and neural network-based signal classification accuracy.

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