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- W4287236415 abstract "Multi-Layer Perceptrons (MLPs) make powerful functional representations for sampling and reconstruction problems involving low-dimensional signals like images,shapes and light fields. Recent works have significantly improved their ability to represent high-frequency content by using periodic activations or positional encodings. This often came at the expense of generalization: modern methods are typically optimized for a single signal. We present a new representation that generalizes to multiple instances and achieves state-of-the-art fidelity. We use a dual-MLP architecture to encode the signals. A synthesis network creates a functional mapping from a low-dimensional input (e.g. pixel-position) to the output domain (e.g. RGB color). A modulation network maps a latent code corresponding to the target signal to parameters that modulate the periodic activations of the synthesis network. We also propose a local-functional representation which enables generalization. The signal's domain is partitioned into a regular grid,with each tile represented by a latent code. At test time, the signal is encoded with high-fidelity by inferring (or directly optimizing) the latent code-book. Our approach produces generalizable functional representations of images, videos and shapes, and achieves higher reconstruction quality than prior works that are optimized for a single signal." @default.
- W4287236415 created "2022-07-25" @default.
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- W4287236415 date "2021-04-08" @default.
- W4287236415 modified "2023-10-16" @default.
- W4287236415 title "Modulated Periodic Activations for Generalizable Local Functional Representations" @default.
- W4287236415 doi "https://doi.org/10.48550/arxiv.2104.03960" @default.
- W4287236415 hasPublicationYear "2021" @default.
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