**Fawkes** is an image cloaking system that helps individuals inoculate their images against unauthorized facial recognition models. Fawkes achieves this by helping users add imperceptible pixel-level changes ("cloaks") to their own photos before releasing them. When used to train facial recognition models, these "cloaked" images produce functional models that consistently cause normal images of the user to be misidentified.

**WaveVAE** is a generative audio model that can be used as a vocoder in text-to-speech systems. It is a [VAE](https://paperswithcode.com/method/vae) based model that can be trained from scratch by jointly optimizing the encoder $q\_{\phi}\left(\mathbf{z}|\mathbf{x}, \mathbf{c}\right)$ and decoder $p\_{\theta}\left(\mathbf{x}|\mathbf{z}, \mathbf{c}\right)$, where $\mathbf{z}$ is latent variables and $\mathbf{c}$ is the mel spectrogram conditioner. 

The encoder of WaveVAE $q\_{\phi}\left(\mathbf{z}|\mathbf{x}\right)$ is parameterized by a Gaussian autoregressive [WaveNet](https://paperswithcode.com/method/wavenet) that maps the ground truth audio x into the same length latent representation $\mathbf{z}$. The decoder $p\_{\theta}\left(\mathbf{x}|\mathbf{z}\right)$ is parameterized by the one-step ahead predictions from an inverse autoregressive flow.

The training objective is the ELBO for the observed $\mathbf{x}$ in the VAE.

WaveVAE

Non-Autoregressive Neural Text-to-Speech

Fawkes

Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

**VQ-VAE** is a type of variational autoencoder that uses vector quantisation to obtain a discrete latent representation. It differs from [VAEs](https://paperswithcode.com/method/vae) in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, ideas from vector quantisation (VQ) are incorporated. Using the VQ method allows the model to circumvent issues of posterior collapse - where the latents are ignored when they are paired with a powerful autoregressive decoder - typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes.

Source	Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com