ComplEx model trained with a nuclear norm regularizer

The **Rectified Linear Unit N**, or **ReLUN**, is a modification of **[ReLU6](https://paperswithcode.com/method/relu6)** activation function that has trainable parameter **n**.

$$ReLUN(x) = min(max(0, x), n)$$

ReLUN

Trainable Activations for Image Classification

ComplEx-N3

Canonical Tensor Decomposition for Knowledge Base Completion

**HiFi-GAN** is a generative adversarial network for speech synthesis. HiFi-GAN consists of one generator and two discriminators: multi-scale and multi-period discriminators. The generator and discriminators are trained adversarially, along with two additional losses for improving training stability and model performance.

The generator is a fully convolutional neural network. It uses a mel-spectrogram as input and upsamples it through transposed convolutions until the length of the output sequence matches the temporal resolution of raw waveforms. Every [transposed convolution](https://paperswithcode.com/method/transposed-convolution) is followed by a multi-receptive field fusion (MRF) module.

For the discriminator, a multi-period discriminator (MPD) is used consisting of several sub-discriminators each handling a portion of periodic signals of input audio. Additionally, to capture consecutive patterns and long-term dependencies, the multi-scale discriminator (MSD) proposed in [MelGAN](https://paperswithcode.com/method/melgan) is used, which consecutively evaluates audio samples at different levels.

Source	Canonical Tensor Decomposition for Knowledge Base Completion
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com