Random Ensemble Mixture (REM) is an easy to implement extension of [DQN](https://paperswithcode.com/method/dqn) inspired by [Dropout](https://paperswithcode.com/method/dropout). The key intuition behind REM is that if one has access to multiple estimates of Q-values, then a weighted combination of the Q-value estimates is also an estimate for Q-values. Accordingly, in each training step, REM randomly combines multiple Q-value estimates and uses this random combination for robust training.

**StyleGAN2** is a generative adversarial network that builds on [StyleGAN](https://paperswithcode.com/method/stylegan) with several improvements. First, [adaptive instance normalization](https://paperswithcode.com/method/adaptive-instance-normalization) is redesigned and replaced with a normalization technique called [weight demodulation](https://paperswithcode.com/method/weight-demodulation). Secondly, an improved training scheme upon progressively growing is introduced, which achieves the same goal - training starts by focusing on low-resolution images and then progressively shifts focus to higher and higher resolutions - without changing the network topology during training. Additionally, new types of regularization like lazy regularization and [path length regularization](https://paperswithcode.com/method/path-length-regularization) are proposed.

StyleGAN2

Analyzing and Improving the Image Quality of StyleGAN

An Optimistic Perspective on Offline Reinforcement Learning

**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. 

This CNN is used as the backbone for [YOLOv4](https://paperswithcode.com/method/yolov4).

Source	An Optimistic Perspective on Offline Reinforcement Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com