FINCH is a parameter-free fast and scalable clustering algorithm. it stands out for its speed and clustering quality.

**Dot-Product Attention** is an attention mechanism where the alignment score function is calculated as: 

$$f_{att}\left(\textbf{h}_{i}, \textbf{s}\_{j}\right) = h\_{i}^{T}s\_{j}$$

It is equivalent to [multiplicative attention](https://paperswithcode.com/method/multiplicative-attention) (without a trainable weight matrix, assuming this is instead an identity matrix). Here $\textbf{h}$ refers to the hidden states for the encoder, and $\textbf{s}$ is the hidden states for the decoder. The function above is thus a type of alignment score function. 

Within a neural network, once we have the alignment scores, we calculate the final scores/weights using a [softmax](https://paperswithcode.com/method/softmax) function of these alignment scores (ensuring it sums to 1).

Dot-Product Attention

Effective Approaches to Attention-based Neural Machine Translation

FINCH Clustering

Efficient Parameter-Free Clustering Using First Neighbor Relations

CDCC-NET is a multi-task network that analyzes the detected counter region and predicts 9 outputs: eight float numbers referring to the corner positions (x0/w, y0/h, ... , x3/w, y3/h) and an array containing two float numbers regarding the probability of the counter being legible/operational or illegible/faulty.

Source	Efficient Parameter-Free Clustering Using First Neighbor Relations
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com