**RepVGG** is a [VGG](https://paperswithcode.com/method/vgg)-style convolutional architecture. It has the following advantages:

- The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes
the output of its only preceding layer as input and feeds the output into its only following layer.
- The model’s body uses only 3 × 3 conv and [ReLU](https://paperswithcode.com/method/relu).
- The concrete architecture (including the specific depth and layer widths) is instantiated with no automatic
search, manual refinement, compound scaling, nor other heavy designs.

Self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.

Magnification Prior Contrastive Similarity

Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images

RepVGG

RepVGG: Making VGG-style ConvNets Great Again

reSGLD proposes to simulate a high-temperature particle for exploration and a low-temperature particle for exploitation and allows them to swap simultaneously. Moreover, a correction term is included to avoid biases.

Source	RepVGG: Making VGG-style ConvNets Great Again
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com