**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.

**WaveTTS** is a [Tacotron](https://paperswithcode.com/method/tacotron)-based text-to-speech architecture that has two loss functions: 1) time-domain loss, denoted as the waveform loss, that measures the distortion between the natural and generated waveform; and 2) frequency-domain loss, that measures the Mel-scale acoustic feature loss between the natural and generated acoustic features.

The motivation arises from [Tacotron 2](https://paperswithcode.com/method/tacotron-2). Here its feature prediction network is trained independently of the [WaveNet](https://paperswithcode.com/method/wavenet) vocoder. At run-time, the feature prediction network and WaveNet vocoder are artificially joined together. As a result, the framework suffers from the mismatch between frequency-domain acoustic features and time-domain waveform. To overcome such mismatch, WaveTTS uses a joint time-frequency domain loss for TTS that effectively improves the synthesized voice quality.

WaveTTS

WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss

TSRUp

Transformation-based Adversarial Video Prediction on Large-Scale Data

**Panoptic-PolarNet** is a point cloud segmentation framework for LiDAR point clouds. It learns both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation, enabling the authors to circumvent the issue of occlusion among instances in urban street scenes. We first encode the raw point cloud data with $K$ features into a fixed-size representation on the polar BEV map. Next, we use a single backbone encoder-decoder network to generate semantic prediction, center [heatmap](https://paperswithcode.com/method/heatmap) and offset regression. Finally, we merge these outputs via a voting-based fusion to yield the panoptic segmentation result.

Source	Transformation-based Adversarial Video Prediction on Large-Scale Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com