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Sinusoidal Representation Network

Siren

9 papers tagged archive 2025-07-28

Introduced by Vincent Sitzmann et al. in Implicit Neural Representations with Periodic Activation Functions

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Siren, or Sinusoidal Representation Network, is a periodic activation function for implicit neural representations. Specifically it uses the sine as a periodic activation function:

Φ(x) = Wₙ(ϕₙ₋₁ ∘ϕₙ₋₂ ∘…∘ϕ₀ )

PaperSource

Papers archive 2025-07-28

9 shown of 9, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Neural Rendering2
Color Manipulation1
Image Compression1
Image Enhancement1
Image Inpainting1
Knowledge Graphs1
Normalising Flows1
Photo Retouching1
Probabilistic Programming1
Red Teaming1
Tone Mapping1

Usage over time archive 2025-07-28

Papers per year tagged with Siren: 2020 to 2025, peak 3 3 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 3 papers 2023 2024: 3 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (9 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Activation Functions

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