Decoder architecture inspired on the [UNet++](https://paperswithcode.com/method/unet) structure and the [EfficientNet](https://paperswithcode.com/method/efficientnet) building blocks. Keeping the UNet++ structure, the EfficientUNet++ achieves higher performance and significantly lower computational complexity through two simple modifications:

* Replaces the 3x3 convolutions of the UNet++ with residual bottleneck blocks with depthwise convolutions
* Applies channel and spatial attention to the bottleneck feature maps using [concurrent spatial and channel squeeze & excitation (scSE)](https://paperswithcode.com/method/scse) blocks

Spatial CNN with UNet based Encoder-decoder and ConvLSTM

SCNN_UNet_ConvLSTM

A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection

EfficientUNet++

Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation

AlphaFold is a deep learning based algorithm for accurate protein structure prediction. AlphaFold incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

Description from: [Highly accurate protein structure prediction with AlphaFold](https://paperswithcode.com/paper/highly-accurate-protein-structure-prediction)

Image credit: [DeepMind](https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology)

Source	Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation
Year	2000
Data Source	Papers with Code archive (CC BY-SA)