**DU-GAN** is a [generative adversarial network](https://www.paperswithcode.com/methods/category/generative-adversarial-networks) for LDCT denoising in medical imaging. The generator produces denoised LDCT images, and two independent branches with [U-Net](https://paperswithcode.com/method/u-net) based discriminators perform at the image and gradient domains. The U-Net based discriminator provides both global structure and local per-pixel feedback to the generator. Furthermore, the image discriminator encourages the generator to produce photo-realistic CT images while the gradient discriminator is utilized for better edge and alleviating streak artifacts caused by photon starvation.

**Conditional Relation Network**, or **CRN**, is a building block to construct more sophisticated structures for representation and reasoning over video. CRN takes as input an array of tensorial objects and a conditioning feature, and computes an array of encoded output objects. Model building becomes a simple exercise of replication, rearrangement and stacking of these reusable units for diverse modalities and contextual information. This design thus supports high-order relational and multi-step reasoning.

Hierarchical Conditional Relation Networks for Video Question Answering

DU-GAN

DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising

**LIMix**, or **Lifelong Infinite Mixture**, is a lifelong learning model which grows a mixture of models to adapt to an increasing number of tasks.  LIMix can automatically expand its network architectures or choose an appropriate component to adapt its parameters for learning a new task, while preserving its previously learnt information. Knowledge is incorporated by means of Dirichlet processes by using a gating mechanism which computes the dependence between the knowledge learnt previously and stored in each component, and a new set of data. Besides, a Student model is trained which can accumulate cross-domain representations over time and make quick inferences.

Source	DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com