A **Spatially Separable Convolution** decomposes a [convolution](https://paperswithcode.com/method/convolution) into two separate operations. In regular convolution, if we have a 3 x 3 kernel then we directly convolve this with the image. We can divide a 3 x 3 kernel into a 3 x 1 kernel and a 1 x 3 kernel. Then, in spatially separable convolution, we first convolve the 3 x 1 kernel then the 1 x 3 kernel. This requires 6 instead of 9 parameters compared to regular convolution, and so it is more parameter efficient (additionally less matrix multiplications are required).

Image Source: [Kunlun Bai](https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215)

Blended Diffusion enables a zero-shot local text-guided image editing of natural images.
Given an input image $x$, an input mask $m$ and a target guiding text $t$ - the method enables to change the masked area within the image corresponding the the guiding text s.t. the unmasked area is left unchanged.

Blended Diffusion

Blended Diffusion for Text-driven Editing of Natural Images

Spatially Separable Convolution

AGCN is a novel spectral graph convolution network that feed on original data of diverse graph structures.

Image credit: [Adaptive Graph Convolutional Neural Networks](https://arxiv.org/pdf/1801.03226.pdf)

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com