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Representing a signal as a continuous function parameterized by neuralnetwork (a.k.a. Implicit Neural Representations, INRs) has attracted increasingattention in recent years. Neural Processes (NPs), which model thedistributions over functions conditioned on partial observations (context set),provide a practical solution for fast inference of continuous functions.However, existing NP architectures suffer from inferior modeling capability forcomplex signals. In this paper, we propose an efficient NP framework dubbedVersatile Neural Processes (VNP), which largely increases the capability ofapproximating functions. Specifically, we introduce a bottleneck encoder thatproduces fewer and informative context tokens, relieving the high computationalcost while providing high modeling capability. At the decoder side, wehierarchically learn multiple global latent variables that jointly model theglobal structure and the uncertainty of a function, enabling our model tocapture the distribution of complex signals. We demonstrate the effectivenessof the proposed VNP on a variety of tasks involving 1D, 2D and 3D signals.Particularly, our method shows promise in learning accurate INRs w.r.t. a 3Dscene without further finetuning. Code is available athttps://github.com/ZongyuGuo/Versatile-NP .

Versatile Neural Processes for Learning Implicit Neural Representations