**HDCGAN**, or **High-resolution Deep Convolutional Generative Adversarial Networks**, is a [DCGAN](https://paperswithcode.com/method/dcgan) based architecture that achieves high-resolution image generation through the proper use of [SELU](https://paperswithcode.com/method/selu) activations. Glasses, a mechanism to arbitrarily improve the final [GAN](https://paperswithcode.com/method/gan) generated results by enlarging the input size by a telescope ζ is also set forth. 

A video showing the training procedure on CelebA-hq can be found [here](https://youtu.be/1XZB87W0SaY).

**MADDPG**, or **Multi-agent DDPG**, extends [DDPG](https://paperswithcode.com/method/ddpg) into a multi-agent policy gradient algorithm where decentralized agents learn a centralized critic based on the observations and actions of all agents. It leads to learned policies that only use local information (i.e. their own observations) at execution time, does not assume a differentiable model of the environment dynamics or any particular structure on the communication method between agents, and is applicable not only to cooperative interaction but to competitive or mixed interaction involving both physical and communicative behavior. The critic is augmented with extra information about the policies of other agents, while the actor only has access to local information. After training is completed, only the local actors are used at execution phase, acting in a decentralized manner.

MADDPG

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

HDCGAN

High-Resolution Deep Convolutional Generative Adversarial Networks

**R-CNN**, or **Regions with CNN Features**, is an object detection model that uses high-capacity CNNs to bottom-up region proposals in order to localize and segment objects. It uses [selective search](https://paperswithcode.com/method/selective-search) to identify a number of bounding-box object region candidates (“regions of interest”), and then extracts features from each region independently for classification.

Source	High-Resolution Deep Convolutional Generative Adversarial Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com