Please enter a description about the method here

**$k$-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)

k-NN

iGCL

Graph Contrastive Learning with Implicit Augmentations

The **WaveGrad UBlock** is used for upsampling in [WaveGrad](https://paperswithcode.com/method/wavegrad). Neural audio generation models often use large receptive field. Dilation factors of four convolutional layers are 1, 2, 1, 2 for the first two UBlocks and 1, 2, 4, 8 for the rest. Orthogonal initialization is used.

Source	Graph Contrastive Learning with Implicit Augmentations
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com