**MPRNet** is a multi-stage progressive image restoration architecture that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, the model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, a per-pixel adaptive design is introduced that leverages in-situ supervised attention to reweight the local features.

**ENet** is a semantic segmentation architecture which utilises a compact encoder-decoder architecture. Some design choices include:

1. Using the [SegNet](https://paperswithcode.com/method/segnet) approach to downsampling y saving indices of elements chosen in max
pooling layers, and using them to produce sparse upsampled maps in the decoder.
2.  Early downsampling to optimize the early stages of the network and reduce the cost of processing large input frames. The first two blocks of ENet heavily reduce the input size, and use only a small set of feature maps. 
3. Using PReLUs as an activation function
4. Using dilated convolutions 
5. Using Spatial [Dropout](https://paperswithcode.com/method/dropout)

ENet

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

MPRNet

Multi-Stage Progressive Image Restoration

**Slot Attention** is an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. Using an iterative attention mechanism, slots produces a set of output vectors with permutation symmetry. Unlike capsules used in Capsule Networks, slots produced by Slot Attention do not specialize to one particular type or class of object, which could harm generalization. Instead, they act akin to object files, i.e., slots use a common representational format: each slot can store (and bind to) any object in the input. This allows Slot Attention to generalize in a systematic way to unseen compositions, more objects, and more slots.

Source	Multi-Stage Progressive Image Restoration
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com