**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)

**GroIE** is an RoI extractor which intends to overcome the limitation of existing extractors which select only one (the best) layer from the [FPN](https://paperswithcode.com/method/fpn). The intuition is that all the layers of FPN retain useful
information. Therefore, the proposed layer introduces non-local building blocks and attention mechanisms to boost the performance.

GRoIE

A novel Region of Interest Extraction Layer for Instance Segmentation

PaLM

PaLM: Scaling Language Modeling with Pathways

**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).

Source	PaLM: Scaling Language Modeling with Pathways
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com