**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.

**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).

HDCGAN

High-Resolution Deep Convolutional Generative Adversarial Networks

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

Adaptive Early-Learning Correction for Segmentation from Noisy Annotations

Source	Rich feature hierarchies for accurate object detection and semantic segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com