TSRUs, also known as Transformation-based Spatial Recurrent Unit, is a type of modification used in the TriVD-GAN architecture for video generation. It is based on TSRUc and is calculated in a fully sequential process. TSRUs are used to make informed decisions prior to mixing the outputs.
What is TSRUs?
TSRUs are a type of modification used in the TriVD-GAN architecture to generate videos. They are a modification of the ConvGRU and are computed in a fully sequential manner with each intermediate output being calculated in real-time. TSRUs are designed to make informed decisions by accessing the previous output and the computed value of c. The modified $u$ equation of TSRUs is:
$$ u = \sigma\left(W\_{u} \star\_{n}\left[\hat{h}\_{t-1};c\right] + b\_{u} \right) $$
In this equation, $\sigma$ and $\rho$ are the elementwise sigmoid and ReLU functions, respectively, and $\star\_{n}$ represents convolution with a kernel of size $n \times n$. The brackets denote a feature concatenation. The modified equation is used to calculate the intermediate value of $u$.
How TSRUs Work?
TSRUs follow the same basic principles as other types of recurrent neural networks. However, they use a few unique modifications to improve their performance. The modified $u$ equation is one such modification that enables TSRUs to make informed decisions based on the previous output and the computed value of c. This modification helps to improve the accuracy of TSRUs and, as a result, enhance the quality of the generated videos.
TSRUs are used in the TriVD-GAN architecture for video generation. This architecture uses a generator network to generate video frames by sampling from a learned probability distribution. The generator network is composed of several neural networks, including TSRUs, that help to improve the quality of the generated videos.
TSRUs work by using a transformation-based mechanism to generate intermediate output. This mechanism is based on the computed value of c and is used to make informed decisions when mixing the outputs. The modified $u$ equation allows TSRUs to take into account the previous output and the computed value of c when generating intermediate output. This results in a more accurate and efficient method for generating video frames.
Benefits of using TSRUs in Video Generation
TSRUs have several benefits when used in video generation networks such as the TriVD-GAN architecture. TSRUs are designed to make informed decisions prior to mixing the outputs, which helps to improve the accuracy and quality of the generated videos. The modified equation of TSRUs is specifically designed to take into account the previous output and the computed value of c. This enables TSRUs to generate intermediate output more accurately and efficiently.
TSRUs are also a fully sequential calculation, which means they are calculated in real-time without any additional computation time. This is important for video generation networks, which need to generate new video frames in real-time.
Overall, TSRUs are an effective modification of the ConvGRU that can be used to improve the quality of generated videos.
