**Discrete Cosine Transform (DCT)** is an orthogonal transformation method that decomposes an
image to its spatial frequency spectrum. It expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. It is used a lot in compression tasks, e..g image compression where for example high-frequency components can be discarded. It is a type of Fourier-related Transform, similar to discrete fourier transforms (DFTs), but only using real numbers.

Image Credit: [Wikipedia](https://en.wikipedia.org/wiki/Discrete_cosine_transform#/media/File:Example_dft_dct.svg)

Discrete Cosine Transform

L-GCN

The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks

A **DiCE Unit** is an image model block that is built using dimension-wise convolutions and dimension-wise fusion.  The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the DiCE unit to efficiently encode spatial and channel-wise information contained in the input tensor. 

Standard convolutions encode spatial and channel-wise information simultaneously, but they are computationally expensive. To improve the efficiency of standard convolutions, separable [convolution](https://paperswithcode.com/method/convolution) are introduced, where spatial and channelwise information are encoded separately using depth-wise and point-wise convolutions, respectively. Though this factorization is effective, it puts a significant computational load on point-wise convolutions and makes them a computational bottleneck.

DiCE Units utilize a dimension-wise convolution to encode depth-wise, width-wise, and height-wise information independently. The dimension-wise convolutions encode local information from different dimensions of the input tensor, but do not capture global information. One approach is a [pointwise convolution](https://paperswithcode.com/method/pointwise-convolution), but it is computationally expensive, so instead dimension-wise fusion factorizes the point-wise convolution in two steps: (1) local fusion and (2) global fusion.

Source	The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Learnable adjacency matrix GCN?

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