**DV3 Attention Block** is an attention-based module used in the [Deep Voice 3](https://paperswithcode.com/method/deep-voice-3) architecture. It uses a [dot-product attention](https://paperswithcode.com/method/dot-product-attention) mechanism. A query vector (the hidden states of the decoder) and the per-timestep key vectors from the encoder are used to compute attention weights. This then outputs a context vector computed as the weighted average of the value vectors.

**Morphence** is an approach for adversarial defense that shifts the defense landscape by making a model a moving target against adversarial examples. By regularly moving the decision function of a model, Morphence makes it significantly challenging for repeated or correlated attacks to succeed. Morphence deploys a pool of models generated from a base model in a manner that introduces sufficient randomness when it responds to prediction queries. To ensure repeated or correlated attacks fail, the deployed pool of models automatically expires after a query budget is reached and the model pool is replaced by a new model pool generated in advance.

Morphence

Morphence: Moving Target Defense Against Adversarial Examples

DV3 Attention Block

Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning

COP-KMeans is a modified version the popular k-means algorithm that supports pairwise constraints. 

Original paper : Constrained K-means Clustering with Background Knowledge, Wagstaff et al. 2001

Source	Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com