**$k$-Nearest Neighbors** is a clustering-based algorithm for classification and regression. It is a a type of instance-based learning as it does not attempt to construct a general internal model, but simply stores instances of the training data. Prediction is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.

Source of Description and Image: [scikit-learn](https://scikit-learn.org/stable/modules/neighbors.html#classification)

**Latent Optimisation** is a technique used for generative adversarial networks to refine the sample quality of $z$. Specifically, it exploits knowledge from the discriminator $D$ to refine the latent source $z$. Intuitively, the gradient $\nabla\_{z}f\left(z\right) = \delta{f}\left(z\right)\delta{z}$ points in the direction that better satisfies the discriminator $D$, which implies better samples. Therefore, instead of using the randomly sampled $z \sim p\left(z\right)$, we uses the optimised latent:

$$ \Delta{z} = \alpha\frac{\delta{f}\left(z\right)}{\delta{z}} $$

$$ z' = z + \Delta{z} $$

Source: [LOGAN](https://paperswithcode.com/method/logan)
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Latent Optimisation

Deep Compressed Sensing

k-NN

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Year	2000
Data Source	CC BY-SA - https://paperswithcode.com