**Center Pooling** is a pooling technique for object detection that aims to capture richer and more recognizable visual patterns. The geometric centers of objects do not necessarily convey very recognizable visual patterns (e.g., the human head contains strong visual patterns, but the center keypoint is often in the middle of the human body). 

The detailed process of center pooling is as follows: the backbone outputs a feature map, and to determine if a pixel in the feature map is a center keypoint, we need to find the maximum value in its both horizontal and vertical directions and add them together. By doing this, center pooling helps the better detection of center keypoints.

**End-to-End Neural Diarization** is a neural network for speaker diarization in which a neural network directly outputs speaker diarization results given a multi-speaker recording. To realize such an end-to-end model, the speaker diarization problem is formulated as a multi-label classification problem and a permutation-free objective function is introduced to directly minimize diarization errors. The EEND method can explicitly handle speaker overlaps during training and inference. Just by feeding multi-speaker recordings with corresponding speaker segment labels, the model can be adapted to real conversations.

EEND

End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification

Center Pooling

CenterNet: Keypoint Triplets for Object Detection

**NADAM**, or **Nesterov-accelerated Adaptive Moment Estimation**, combines [Adam](https://paperswithcode.com/method/adam) and [Nesterov Momentum](https://paperswithcode.com/method/nesterov-accelerated-gradient). The update rule is of the form:

$$ \theta\_{t+1} = \theta\_{t} - \frac{\eta}{\sqrt{\hat{v}\_{t}}+\epsilon}\left(\beta\_{1}\hat{m}\_{t} + \frac{(1-\beta\_{t})g\_{t}}{1-\beta^{t}\_{1}}\right)$$

Image Source: [Incorporating Nesterov Momentum into Adam](http://cs229.stanford.edu/proj2015/054_report.pdf)

Source	CenterNet: Keypoint Triplets for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com