**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.

**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.

DU-GAN

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

LIMix

Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process

**Embedded Dot Product Affinity** is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a dot product function in an embedding space:

$$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = \theta\left(\mathbb{x\_{i}}\right)^{T}\phi\left(\mathbb{x\_{j}}\right) $$

Here $\theta\left(x\_{i}\right) = W\_{θ}x\_{i}$ and $\phi\left(x\_{j}\right) = W\_{φ}x\_{j}$ are two embeddings.

The main difference between the dot product and [embedded Gaussian affinity](https://paperswithcode.com/method/embedded-gaussian-affinity) functions is the presence of [softmax](https://paperswithcode.com/method/softmax), which plays the role of an activation function.

Source	Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com