**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.

**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.

Parametric UMAP

Parametric UMAP embeddings for representation and semi-supervised learning

Fully Convolutional Networks for Semantic Segmentation

The **Pointer Sentinel-LSTM mixture model** is a type of recurrent neural network that combines the advantages of standard [softmax](https://paperswithcode.com/method/softmax) classifiers with those of a pointer component for effective and efficient language modeling. Rather than relying on the RNN hidden state to decide when to use the pointer, the model allows the pointer component itself to decide when to use the softmax vocabulary through a sentinel.

Source	Fully Convolutional Networks for Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com