Given a training set drawn from an unknown $d$-variate probability distribution, QuantTree constructs a histogram by recursively splitting $\mathbb{R}^d$. The splits are defined by a stochastic process so that each bin contains a certain proportion of the training set. These histograms can be used to define test statistics (e.g., the Pearson statistic) to tell whether a batch of data is drawn from $\phi_0$ or not. The most crucial property of QuantTree is that the distribution of any statistic based on QuantTree histograms is independent of $\phi_0$, thus enabling nonparametric statistical testing.

**IFBlock** is a video model block used in the [IFNet](https://paperswithcode.com/method/ifnet) architecture for video frame interpolation. IFBlocks do not contain expensive operators like cost volume or forward warping and use 3 × 3 convolution and deconvolution as building blocks. Each IFBlock has a feed-forward structure consisting of several convolutional layers and an upsampling operator. Except for the layer that outputs the optical flow residuals and the fusion map, [PReLU](https://paperswithcode.com/method/prelu) activations are used.

IFBlock

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

QuantTree

QuantTree: Histograms for Change Detection in Multivariate Data Streams

**PointAugment** is a an auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier.

Source	QuantTree: Histograms for Change Detection in Multivariate Data Streams
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com