UNet++ is an architecture for semantic segmentation based on the [U-Net](https://paperswithcode.com/method/u-net). Through the use of densely connected nested decoder sub-networks, it enhances extracted feature processing and was reported by its authors to outperform the U-Net in [Electron Microscopy (EM)](https://imagej.net/events/isbi-2012-segmentation-challenge), [Cell](https://acsjournals.onlinelibrary.wiley.com/doi/full/10.1002/cncy.21576), [Nuclei](https://www.kaggle.com/c/data-science-bowl-2018), [Brain Tumor](https://paperswithcode.com/dataset/brats-2013-1), [Liver](https://paperswithcode.com/dataset/lits17) and [Lung Nodule](https://paperswithcode.com/dataset/lidc-idri) medical image segmentation tasks.

**context2vec** is an unsupervised model for learning generic context embedding of wide sentential contexts, using a bidirectional [LSTM](https://paperswithcode.com/method/lstm). A large plain text corpora is trained on to learn a neural model that embeds entire sentential contexts and target words in the same low-dimensional space, which
is optimized to reflect inter-dependencies between targets and their entire sentential context as a whole. 

In contrast to word2vec that use context modeling mostly internally and considers the target word embeddings as their main output, the focus of context2vec is the context representation. context2vec achieves its objective by assigning similar embeddings to sentential contexts and their associated target words.

context2vec

context2vec: Learning Generic Context Embedding with Bidirectional LSTM

UNet++

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

**SGDW** is a stochastic optimization technique that decouples [weight decay](https://paperswithcode.com/method/weight-decay) from the gradient update:

$$ g\_{t} =  \nabla{f\_{t}}\left(\theta\_{t-1}\right) + \lambda\theta\_{t-1}$$

$$ m\_{t} =  \beta\_{1}m\_{t-1} + \eta\_{t}\alpha{g}\_{t}$$

$$ \theta\_{t} = \theta\_{t-1} - m\_{t} - \eta\_{t}\lambda\theta\_{t-1}$$

Source	UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com