Recurrent Neural Networks
TSRUp
Introduced by Luc et al. in Transformation-based Adversarial Video Prediction on Large-Scale DataTSRUp, 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 $\theta$, $u$ 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 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.
Source: Transformation-based Adversarial Video Prediction on Large-Scale Data
Papers
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Usage Over Time
Components
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Convolution
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Convolutions | |
Dense Connections
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Feedforward Networks | |
Max Pooling
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Pooling Operations | |
ReLU
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Activation Functions | |
Sigmoid Activation
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Activation Functions |
