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arXiv Machine Learning By David Dinkevich, Nisan Chiprut, Yoav HaCohen, Dani Lischinski

APEX: Asynchronous Prefix Denoising for Extreme Compression

Read the original on arXiv Machine Learning →

APEX (Asynchronous Prefix denoising for EXtreme compression) introduces a hierarchical latent space paired with asynchronous denoising over channel groups, enabling efficient extreme compression of images. The method orders channel prefixes from structure to detail, allowing a shared denoiser to generate them at staggered local times without adding tokens. On ImageNet-512, APEX outperforms existing methods at every evaluated channel count under 64x and 128x compression, and surpasses a 256-token model across all budgets when matched for training and guided sampling compute, especially at lower compute levels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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