A **SENet** is a convolutional neural network architecture that employs squeeze-and-excitation blocks to enable the network to perform dynamic channel-wise feature recalibration.

**ScanSSD** is a single-shot Detector ([SSD](https://paperswithcode.com/method/ssd)) for locating math formulas offset from text and embedded in textlines. It uses only visual features for detection: no formatting or typesetting information such as layout, font, or character labels are employed. Given a 600 dpi document page image, a Single Shot Detector (SSD) locates formulas at multiple scales using sliding windows, after which candidate detections are pooled to obtain page-level results.

ScanSSD

ScanSSD: Scanning Single Shot Detector for Mathematical Formulas in PDF Document Images

SENet

Squeeze-and-Excitation Networks

**Adaptive Instance Normalization** is a normalization method that aligns the mean and variance of the content features with those of the style features. 

[Instance Normalization](https://paperswithcode.com/method/instance-normalization) normalizes the input to a single style specified by the affine parameters. Adaptive Instance Normaliation is an extension. In AdaIN, we receive a content input $x$ and a style input $y$, and we simply align the channel-wise mean and variance of $x$ to match those of $y$. Unlike [Batch Normalization](https://paperswithcode.com/method/batch-normalization), Instance Normalization or [Conditional Instance Normalization](https://paperswithcode.com/method/conditional-instance-normalization), AdaIN has no learnable affine parameters. Instead, it adaptively computes the affine parameters from the style input:

$$
\textrm{AdaIN}(x, y)= \sigma(y)\left(\frac{x-\mu(x)}{\sigma(x)}\right)+\mu(y)
$$

Source	Squeeze-and-Excitation Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com