**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).

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.

An Optimistic Perspective on Offline Reinforcement Learning

CSPDarknet53

YOLOv4: Optimal Speed and Accuracy of Object Detection

**Local Contrast Normalization** is a type of normalization that performs local subtraction and division normalizations, enforcing a sort of local competition between adjacent features in a feature map, and between features at the same spatial location in different feature maps.

Source	YOLOv4: Optimal Speed and Accuracy of Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com