**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.

**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.

LIMix

Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process

Embedded Dot Product Affinity

Non-local Neural Networks

**Sample Redistribution** is a [data augmentation](https://paperswithcode.com/methods/category/image-data-augmentation) technique for face detection which augments training samples based on the statistics of benchmark datasets via large-scale cropping. During training data augmentation, square patches are cropped from the original images with a random size from the set $[0.3,1.0]$ of the short edge of the original images. To generate more positive samples for stride 8, the random size range is enlarged from $[0.3,1.0]$ to $[0.3,2.0]$. When the crop box is beyond the original image, average RGB values fill the missing pixels.

The motivation is that for efficient [face detection](https://paperswithcode.com/task/face-detection) under a fixed VGA resolution (i.e. 640×480), most of the faces (78.93%) in [WIDER FACE](https://paperswithcode.com/dataset/wider-face-1) are smaller than 32×32 pixels, and thus they are predicted by shallow stages. To obtain more training samples for these shallow stages, Sample Redistribution (SR) is used.

Source	Non-local Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com