**CS-GAN** is a type of generative adversarial network that uses a form of deep compressed sensing, and [latent optimisation](https://paperswithcode.com/method/latent-optimisation), to improve the quality of generated samples.

**Hamburger** is a global context module that employs matrix decomposition to factorize the learned representation into sub-matrices so as to recover the clean low-rank signal subspace. The key idea is, if we formulate the inductive bias like the global context into an objective function, the optimization algorithm to minimize the objective function can construct a computational graph, i.e., the architecture we need in the networks.

Hamburger

Is Attention Better Than Matrix Decomposition?

CS-GAN

Deep Compressed Sensing

TuckER model trained with a relation prediction objective on top of the 1vsAll loss

Source	Deep Compressed Sensing
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com