**Wasserstein GAN**, or **WGAN**, is a type of generative adversarial network that minimizes an approximation of the Earth-Mover's distance (EM) rather than the Jensen-Shannon divergence as in the original [GAN](https://paperswithcode.com/method/gan) formulation. It leads to more stable training than original GANs with less evidence of mode collapse, as well as meaningful curves that can be used for debugging and searching hyperparameters.

**SPP-Net** is a convolutional neural architecture that employs [spatial pyramid pooling](https://paperswithcode.com/method/spatial-pyramid-pooling) to remove the fixed-size constraint of the network. Specifically, we add an SPP layer on top of the last convolutional layer. The SPP layer pools the features and generates fixed-length outputs, which are then fed into the fully-connected layers (or other classifiers). In other words, we perform some information aggregation at a deeper stage of the network hierarchy (between convolutional layers and fully-connected layers) to avoid the need for cropping or warping at the beginning.

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

WGAN

Wasserstein GAN

**ManifoldPlus** is a method for robust and scalable conversion of triangle soups to watertight manifolds. It extracts exterior faces between occupied voxels and empty voxels, and uses a projection based optimization method to accurately recover a watertight manifold that resembles the reference mesh. It does not rely on face normals of the input triangle soups and can accurately recover zero-volume structures. For scalability, it employs an adaptive Gauss-Seidel method for shape optimization, in which each step is an easy-to-solve convex problem.

Source	Wasserstein GAN
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com