Transcription of Implicit Neural Representations with Periodic Activation …
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Implicit Neural Representations with Periodic Activation …
Implicit Neural Representations with Periodic Activation Functions Vincent Sitzmann Julien N. P. Martel Alexander W. Bergman [ ] 17 Jun 2020. David B. Lindell Gordon Wetzstein Stanford University Abstract Implicitly defined, continuous, differentiable signal Representations parameterized by Neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional Representations . However, current network architectures for such Implicit Neural Representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations.
Implicit neural representations. Recent work has demonstrated the potential of fully connected networks as continuous, memory-efficient implicit representations for shape parts [6, 7], objects [1, 4, 8, 9], or scenes [10–13]. These representations are typically trained from …
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