Methods › Sequential › Recurrent Neural Networks › TSRUc
TSRUc
Introduced by Pauline Luc et al. in Transformation-based Adversarial Video Prediction on Large-Scale Data
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
TSRUc, or Transformation-based Spatial Recurrent Unit c, is a modification of a ConvGRU used in the TriVD-GAN architecture for video generation.
Instead of computing the reset gate r and resetting hₜ₋₁, the TSRUc computes the parameters of a transformation θ, which we use to warp hₜ₋₁. The rest of our model is unchanged (with ĥₜ₋₁ playing the role of h′ₜ in c’s update equation from ConvGRU. The TSRUc module is described by the following equations:
θ_(h,x) = f(hₜ₋₁, xₜ)
ĥₜ₋₁ = w(hₜ₋₁; θ_(h, x))
c = ρ(W_c ⋆ₙ[ĥₜ₋₁;xₜ] + b_c )
u = σ(Wᵤ ⋆ₙ[hₜ₋₁;xₜ] + bᵤ )
hₜ = u ⊙hₜ₋₁ + (1-u) ⊙c
In these equations σ and ρ are the elementwise sigmoid and ReLU functions respectively and the ⋆ₙ represents a convolution with a kernel of size n ×n. Brackets are used to represent a feature concatenation.
Papers archive 2025-07-28
1 shown of 1, 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.
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Transformation-based Adversarial Video Prediction on Large-Scale Data 9 Mar 2020 · 0 repositories · arXiv:2003.04035
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Prediction | 1 |
| Video Generation | 1 |
| Video Prediction | 1 |
Usage over time archive 2025-07-28
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
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