**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.

**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).

CSPDarknet53

YOLOv4: Optimal Speed and Accuracy of Object Detection

Local Contrast Normalization

BYOL (Bootstrap Your Own Latent) is a new approach to self-supervised learning. BYOL’s goal is to learn a representation $y_θ$ which can then be used for downstream tasks. BYOL uses two neural networks to learn: the online and target networks. The online network is defined by a set of weights $θ$ and is comprised of three stages: an encoder $f_θ$, a projector $g_θ$ and a predictor $q_θ$. The target network has the same architecture
as the online network, but uses a different set of weights $ξ$. The target network provides the regression
targets to train the online network, and its parameters $ξ$ are an exponential moving average of the
online parameters $θ$.

Given the architecture diagram on the right, BYOL minimizes a similarity loss between $q_θ(z_θ)$ and $sg(z'{_ξ})$, where $θ$ are the trained weights, $ξ$ are an exponential moving average of $θ$ and $sg$ means stop-gradient. At the end of training, everything but $f_θ$ is discarded, and $y_θ$ is used as the image representation.

Source: [Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning](https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to-1)

Image credit: [Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning](https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to-1)

Year	2009
Data Source	CC BY-SA - https://paperswithcode.com