Spectral clustering has attracted increasing attention due to
the promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,

**Prescribed GANs** add noise to the output of a density network and optimize an entropy-regularized adversarial loss. The added noise renders tractable approximations of the predictive log-likelihood and stabilizes the training procedure. The entropy regularizer encourages PresGANs to capture all the modes of the data distribution. Fitting PresGANs involves computing the intractable gradients of the [entropy regularization](https://paperswithcode.com/method/entropy-regularization) term; PresGANs sidestep this intractability using
unbiased stochastic estimates.

PresGAN

Prescribed Generative Adversarial Networks

Spectral Clustering

A Tutorial on Spectral Clustering

**OMGD**, or **Online Multi-Granularity Distillation** is a framework for learning efficient [GANs](https://paperswithcode.com/methods/category/generative-adversarial-networks). The student generator is optimized in a discriminator-free and ground-truth-free setting. The scheme trains the teacher and student alternatively, promoting these two generators iteratively and progressively. The progressively optimized teacher generator helps to warm up the student and guide the optimization direction step by step.

Specifically, the student generator $G\_{S}$ only leverages the complementary teacher generators $G^{W}\_{T}$ and $G^{D}\_{T}$ for optimization and can be trained in the discriminator-free and ground-truth-free setting. This framework transfers different levels concepts from the intermediate layers and output layer to perform the knowledge distillation. The whole optimization is conducted on an online distillation scheme. Namely,  $G^{W}\_{T}$, $G^{D}\_{T}$ and $G\_{S}$ are optimized simultaneously and progressively.

Source	A Tutorial on Spectral Clustering
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com