TSRUp
TSRUp
**TSRUp**, or **Transformation-based Spatial Recurrent Unit p**, is a modification of a [ConvGRU](https://paperswithcode.com/method/cgru) used in the [TriVD-GAN](https://paperswithcode.com/method/trivd-gan) architecture for video generation.

It largely follows [TSRUc](https://paperswithcode.com/method/tsruc), but computes $\theta$, $u$ and $c$ in parallel given $x\_{t}$ and $h\_{t−1}$, yielding the following replacement for the $c$ update equation:

$$ c = \rho\left(W\_{c} \star\_{n}\left[h\_{t-1}; x\_{t}\right] + b\_{c} \right) $$

In these equations $\sigma$ and $\rho$ are the elementwise sigmoid and [ReLU](https://paperswithcode.com/method/relu) functions respectively and the $\star\_{n}$ represents a [convolution](https://paperswithcode.com/method/convolution) with a kernel of size $n \times n$. Brackets are used to represent a feature concatenation.
Transformation-based Adversarial Video Prediction on Large-Scale Data
https://paperswithcode.com/paper/transformation-based-adversarial-video
Transformation-based Adversarial Video Prediction on Large-Scale Data