**RealNVP** is a generative model that utilises real-valued non-volume preserving (real NVP) transformations for density estimation. The model can perform efficient and exact inference, sampling and log-density estimation of data points.

**Gaussian Affinity** is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a Gaussian function:

$$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = e^{\mathbb{x^{T}\_{i}}\mathbb{x\_{j}}} $$

Here $\mathbb{x^{T}\_{i}}\mathbb{x\_{j}}$ is dot-product similarity.

Gaussian Affinity

RealNVP

Density estimation using Real NVP

**Canvas Method** is a method for inference attacks on object detection models. It draws a predicted bounding box distribution on an empty canvas for an attack model input. The canvas is initially set to an image of 300$\times$300 pixels in size, where every pixel has a value of zero and the boxes drawn on the canvas have the same center as the predicted boxes and the same intensity as the prediction scores.

Source	Density estimation using Real NVP
Year	2000
Data Source	Papers with Code archive (CC BY-SA)