**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).

**PaLM** (**Pathways Language Model**) uses a standard Transformer model architecture (Vaswani et al., 2017) in a decoder-only setup (i.e., each timestep can only attend to itself and past timesteps), with several modifications. PaLM is trained as a 540 billion parameter, densely activated, autoregressive Transformer on 780 billion tokens. PaLM leverages Pathways (Barham et al., 2022), which enables highly efficient training of very large neural networks across thousands of accelerator chips.

Image credit: [PaLM: Scaling Language Modeling with Pathways](https://paperswithcode.com/paper/palm-scaling-language-modeling-with-pathways-1)

PaLM

PaLM: Scaling Language Modeling with Pathways

PrivacyNet

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

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].

Source	PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com