**Parametric UMAP** is a non-parametric graph-based dimensionality reduction algorithm that extends the second step of [UMAP](https://www.paperswithcode.com/method/umap) to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding.

Attack on image classifiers by a image solarization through greedy random search.

RandSol

Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers

Parametric UMAP

Parametric UMAP embeddings for representation and semi-supervised learning

**Fully Convolutional Networks**, or **FCNs**, are an architecture used mainly for semantic segmentation. They employ solely locally connected layers, such as [convolution](https://paperswithcode.com/method/convolution), pooling and upsampling. Avoiding the use of dense layers means less parameters (making the networks faster to train). It also means an FCN can work for variable image sizes given all connections are local.

The network consists of a downsampling path, used to extract and interpret the context, and an upsampling path, which allows for localization. 

FCNs also employ skip connections to recover the fine-grained spatial information lost in the downsampling path.

Source	Parametric UMAP embeddings for representation and semi-supervised learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com