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Methods/TSRUp

TSRUp

SequentialIntroduced 20001 papers
Source Paper

Description

TSRUp, or Transformation-based Spatial Recurrent Unit p, is a modification of a ConvGRU used in the TriVD-GAN architecture for video generation.

It largely follows TSRUc, but computes θ\thetaθ, uuu and ccc in parallel given x_tx\_{t}x_t and h_t−1h\_{t−1}h_t−1, yielding the following replacement for the ccc update equation:

c=ρ(W_c⋆_n[h_t−1;x_t]+b_c)c = \rho\left(W\_{c} \star\_{n}\left[h\_{t-1}; x\_{t}\right] + b\_{c} \right)c=ρ(W_c⋆_n[h_t−1;x_t]+b_c)

In these equations σ\sigmaσ and ρ\rhoρ are the elementwise sigmoid and ReLU functions respectively and the ⋆_n\star\_{n}⋆_n represents a convolution with a kernel of size n×nn \times nn×n. Brackets are used to represent a feature concatenation.

Papers Using This Method

Transformation-based Adversarial Video Prediction on Large-Scale Data2020-03-09