RFSQ applies learned scaling or invertible LayerNorm to residual FSQ to prevent magnitude decay, reporting DNSMOS and image loss gains, though the LayerNorm variant has a reconstruction inconsistency.
Background: Finite Scalar Quantization FSQ quantizes ad-dimensional vectorz∈R d by indepen- dently quantizing each dimension to a finite set of levels
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Robust Residual Finite Scalar Quantization for Neural Compression
RFSQ applies learned scaling or invertible LayerNorm to residual FSQ to prevent magnitude decay, reporting DNSMOS and image loss gains, though the LayerNorm variant has a reconstruction inconsistency.