**Attention Mesh** is a neural network architecture for 3D face mesh prediction that uses attention to semantically meaningful regions. Specifically region-specific heads are employed that transform the feature maps with spatial transformers.

**Local SGD** is a distributed training technique that runs [SGD](https://paperswithcode.com/method/sgd) independently in parallel on different workers and averages the sequences only once in a while.

Local SGD

Local SGD Converges Fast and Communicates Little

Attention Mesh

Attention Mesh: High-fidelity Face Mesh Prediction in Real-time

**Mirror Descent Policy Optimization (MDPO)** is a policy gradient algorithm based on the idea of iteratively solving a trust-region problem that minimizes a sum of two terms: a linearization of the standard RL objective function and a proximity term that restricts two consecutive updates to be close to each other. It is based on Mirror Descent, which is a general trust region method that
attempts to keep consecutive iterates close to each other.

Source	Attention Mesh: High-fidelity Face Mesh Prediction in Real-time
Year	2000
Data Source	Papers with Code archive (CC BY-SA)