**Phase Shuffle** is a technique for removing pitched noise artifacts that come from using transposed convolutions in audio generation models. Phase shuffle is an operation with hyperparameter $n$. It randomly perturbs the phase of each layer’s activations by −$n$ to $n$ samples before input to the next layer.

In the original application in [WaveGAN](https://paperswithcode.com/method/wavegan), the authors only apply phase shuffle to the discriminator, as the latent vector already provides the generator a mechanism to manipulate the phase
of a resultant waveform. Intuitively speaking, phase shuffle makes the discriminator’s job more challenging by requiring invariance to the phase of the input waveform.

**ShuffleNet** is a convolutional neural network designed specially for mobile devices with very limited computing power. The architecture utilizes two new operations, pointwise group [convolution](https://paperswithcode.com/method/convolution) and [channel shuffle](https://paperswithcode.com/method/channel-shuffle), to reduce computation cost while maintaining accuracy.

ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Phase Shuffle

Adversarial Audio Synthesis

**Cross View Training**, or **CVT**, is a semi-supervised algorithm for training distributed word representations that makes use of unlabelled and labelled examples. 

CVT adds $k$ auxiliary prediction modules to the model, a Bi-[LSTM](https://paperswithcode.com/method/lstm) encoder, which are used when learning on unlabeled examples. A prediction module is usually a small neural network (e.g., a hidden layer followed by a [softmax](https://paperswithcode.com/method/softmax) layer). Each one takes as input an intermediate representation $h^j(x_i)$ produced by the model (e.g., the outputs of one of the LSTMs in a Bi-LSTM model). It outputs a distribution over labels $p\_{j}^{\theta}\left(y\mid{x\_{i}}\right)$.

Each $h^j$ is chosen such that it only uses a part of the input $x_i$; the particular choice can depend on the task and model architecture. The auxiliary prediction modules are only used during training; the test-time prediction come from the primary prediction module that produces $p_\theta$.

Source	Adversarial Audio Synthesis
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com