**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.

ComplEx model trained with a nuclear norm regularizer

ComplEx-N3

Canonical Tensor Decomposition for Knowledge Base Completion

HiFi-GAN

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

**ShuffleNet V2 Downsampling Block** is a block for spatial downsampling used in the [ShuffleNet V2](https://paperswithcode.com/method/shufflenet-v2) architecture. Unlike the regular [ShuffleNet](https://paperswithcode.com/method/shufflenet) V2 block, the channel split operator is removed so the number of output channels is doubled.

Source	HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com