Importance Matching Lemma for Lossy Compression with Side Information
CoRR(2024)
摘要
We propose two extensions to existing importance sampling based methods for
lossy compression. First, we introduce an importance sampling based compression
scheme that is a variant of ordered random coding (Theis and Ahmed, 2022) and
is amenable to direct evaluation of the achievable compression rate for a
finite number of samples. Our second and major contribution is the importance
matching lemma, which is a finite proposal counterpart of the recently
introduced Poisson matching lemma (Li and Anantharam, 2021). By integrating
with deep learning, we provide a new coding scheme for distributed lossy
compression with side information at the decoder. We demonstrate the
effectiveness of the proposed scheme through experiments involving synthetic
Gaussian sources, distributed image compression with MNIST and vertical
federated learning with CIFAR-10.
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