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COMpression with Bayesian Implicit NEural Representations (COMBINER) is arecent data compression method that addresses a key inefficiency of previousImplicit Neural Representation (INR)-based approaches: it avoids quantizationand enables direct optimization of the rate-distortion performance. However,COMBINER still has significant limitations: 1) it uses factorized priors andposterior approximations that lack flexibility; 2) it cannot effectively adaptto local deviations from global patterns in the data; and 3) its performancecan be susceptible to modeling choices and the variational parameters'initializations. Our proposed method, Robust and Enhanced COMBINER(RECOMBINER), addresses these issues by 1) enriching the variationalapproximation while maintaining its computational cost via a linearreparameterization of the INR weights, 2) augmenting our INRs with learnablepositional encodings that enable them to adapt to local details and 3)splitting high-resolution data into patches to increase robustness andutilizing expressive hierarchical priors to capture dependency across patches.We conduct extensive experiments across several data modalities, showcasingthat RECOMBINER achieves competitive results with the best INR-based methodsand even outperforms autoencoder-based codecs on low-resolution images at lowbitrates.

RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations