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Cross-Domain Lossy Compression as Entropy Constrained ...
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由 H Liu 著作2022被引用 6 次 — We study an extension of lossy compression where the reconstruction is subject to a distribution constraint which can be different from the source distribution.
Cross-Domain Lossy Compression as Entropy Constrained ...
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由 H Liu 著作2022被引用 6 次 — Abstract—We study an extension of lossy compression where the reconstruction is subject to a distribution constraint which can be different from the source ...
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Cross-Domain Lossy Compression as Entropy Constrained ...
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2024年10月22日 — We study an extension of lossy compression where the reconstruction is subject to a distribution constraint which can be different from the ...
lossy compression with distribution shift as entropy ...
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由 H Liu 著作被引用 12 次 — Comment: This paper discusses the problem of cross-domain lossy compression on the basis of its reformulation as an entropy-constrained optimal ...
Cross-Domain Lossy Compression as Entropy Constrained ...
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Cross-Domain Lossy Compression as Entropy Constrained Optimal Transport Journal Articles uri icon. Overview; Research; Identity; Additional Document Info ...
Cross-Domain Lossy Compression as Optimal Transport ...
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We study the problem of cross-domain lossy compression where the reconstruction distribution is different from the source distribution in order to account ...
Cross-Domain Lossy Compression as Optimal Transport with an ...
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In this work, we characterize this as an optimal transport problem with constrained entropy between source and target distributions. We show that optimal ...
LOSSY COMPRESSION WITH DISTRIBUTION SHIFT AS ...
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由 H Liu 著作被引用 12 次 — We provide a formulation of lossy compression with distribution shift as a generalization of optimal transport with an entropy constraint and identify the ...
Huan Liu
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Cross-Domain Lossy Compression as Entropy Constrained Optimal Transport. H Liu, G Zhang, J Chen, A Khisti. IEEE Journal on Selected Areas in Information ...
Deep Learning Methods for Inverse Problems
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We study an extension of lossy compression where the reconstruction is subject to a distribution constraint which can be different from the source distribution.