# What is: TSRUs?

Source | Transformation-based Adversarial Video Prediction on Large-Scale Data |

Year | 2000 |

Data Source | CC BY-SA - https://paperswithcode.com |

**TSRUs**, 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 each intermediate output in a fully sequential manner: like in TSRUc, $c$ is given access to $\hat{h}\_{t-1}$, but additionally, $u$ is given access to both outputs $\hat{h}\_{t-1}$ and $c$, so as to make an informed decision prior to mixing. This yields the following replacement for $u$:

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

In these equations $\sigma$ and $\rho$ are the elementwise sigmoid and ReLU functions respectively and the $\star\_{n}$ represents a convolution with a kernel of size $n \times n$. Brackets are used to represent a feature concatenation.