**Zero Redundancy Optimizer (ZeRO)** is a sharded data parallel method for distributed training. ZeRODP removes the memory state redundancies across data-parallel processes by partitioning the model states instead of replicating them, and it retains the compute/communication efficiency by retaining the computational granularity and communication volume of DP using a dynamic communication schedule during training.

**Ape-X** is a distributed architecture for deep reinforcement learning. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared [experience replay](https://paperswithcode.com/method/experience-replay) memory; the learner replays samples of experience and updates the neural network. The architecture relies on [prioritized experience replay](https://paperswithcode.com/method/prioritized-experience-replay) to focus only on the most significant data generated by the actors.

In contrast to Gorila, Ape-X uses a shared, centralized replay memory, and instead of sampling
uniformly, it prioritizes, to sample the most useful data more often. All communications are batched with the centralized replay, increasing the efficiency and throughput at the cost of some latency. 
And by learning off-policy, Ape-X has the ability to combine data from many distributed actors, by giving the different actors different exploration policies, broadening the diversity of the experience they jointly encounter.

Ape-X

Distributed Prioritized Experience Replay

ZeRO

ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

**ARShoe** is a multi-branch network for pose estimation and segmentation tackling the "try-on" problem for augmented reality shoes. Consisting of an encoder and a decoder, the multi-branch network is trained to predict keypoints [heatmap](https://paperswithcode.com/method/heatmap) (heatmap), [PAFs](https://paperswithcode.com/method/pafs) heatmap (pafmap), and segmentation results (segmap) simultaneously. Post processes are then performed for a smooth and realistic virtual try-on.

Source	ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com