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- W3100298849 abstract "Neural-network-based compressors have proven to be remarkably effective at compressing sources, such as images, that are nominally high-dimensional but presumed to be concentrated on a low-dimensional manifold. We consider a continuous-time random process that models an extreme version of such a source, wherein the realizations fall along a one-dimensional curve in function space that has infinite-dimensional linear span. We precisely characterize the optimal entropy-distortion tradeoff for this source and show numerically that it is achieved by neural-network-based compressors trained via stochastic gradient descent. In contrast, we show both analytically and experimentally that compressors based on the classical Karhunen-Loeve transform are highly suboptimal at high rates." @default.
- W3100298849 created "2020-11-23" @default.
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- W3100298849 date "2020-11-10" @default.
- W3100298849 modified "2023-10-16" @default.
- W3100298849 title "Neural Networks Optimally Compress the Sawbridge" @default.
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