CLIPort, a language-conditioned imitation-learning agent that combines the broad semantic understanding (what) of CLIP [1] with the spatial precision (where) of Transporter [2].

**PrivacyNet** is a [GAN](https://paperswithcode.com/method/gan)-based semi-adversarial network (SAN) that modifies an input face image such that it can be used by a face matcher for matching purposes but cannot be reliably used by an attribute classifier. PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e.g., age and race), while allowing for other types of attributes to be extracted (e.g., gender).

PrivacyNet

PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy

CLIPort

CLIPort: What and Where Pathways for Robotic Manipulation

**Wasserstein Gradient Penalty Loss**, or **WGAN-GP Loss**, is a loss used for generative adversarial networks that augments the Wasserstein loss with a gradient norm penalty for random samples $\mathbf{\hat{x}} \sim \mathbb{P}\_{\hat{\mathbf{x}}}$ to achieve Lipschitz continuity:

$$ L = \mathbb{E}\_{\mathbf{\hat{x}} \sim \mathbb{P}\_{g}}\left[D\left(\tilde{\mathbf{x}}\right)\right] - \mathbb{E}\_{\mathbf{x} \sim \mathbb{P}\_{r}}\left[D\left(\mathbf{x}\right)\right] + \lambda\mathbb{E}\_{\mathbf{\hat{x}} \sim \mathbb{P}\_{\hat{\mathbf{x}}}}\left[\left(||\nabla\_{\tilde{\mathbf{x}}}D\left(\mathbf{\tilde{x}}\right)||\_{2}-1\right)^{2}\right]$$

It was introduced as part of the [WGAN-GP](https://paperswithcode.com/method/wgan-gp) overall model.

Source	CLIPort: What and Where Pathways for Robotic Manipulation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com