**PCA Whitening** is a processing step for image based data that makes input less redundant. Adjacent pixel or feature values can be highly correlated, and whitening through the use of [PCA](https://paperswithcode.com/method/pca) reduces this degree of correlation.

Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)

A **Dutch Eligibility Trace** is a type of [eligibility trace](https://paperswithcode.com/method/eligibility-trace) where the trace increments grow less quickly than the accumulative eligibility trace (helping avoid large variance updates). For the memory vector $\textbf{e}\_{t} \in \mathbb{R}^{b} \geq \textbf{0}$:

$$\mathbf{e\_{0}} = \textbf{0}$$

$$\textbf{e}\_{t} = \gamma\lambda\textbf{e}\_{t-1} + \left(1-\alpha\gamma\lambda\textbf{e}\_{t-1}^{T}\phi\_{t}\right)\phi\_{t}$$

Dutch Eligibility Trace

PCA Whitening

**CV-MIM**, or **Contrastive Cross-View Mutual Information Maximization**, is a representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization, which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. It further utilizes two regularization terms to ensure disentanglement and smoothness of the learned representations.

Year	2000
Data Source	Papers with Code archive (CC BY-SA)