**FractalNet** is a type of convolutional neural network that eschews [residual connections](https://paperswithcode.com/method/residual-connection) in favour of a "fractal" design. They involve repeated application of a simple expansion rule to generate deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers.

**Weighted Recurrent Quality Enhancement**, or **WRQE**, is a recurrent quality enhancement network for video compression that takes both compressed frames and the bit stream as inputs. In the recurrent cell of WRQE, the memory and update signal are weighted by quality features to reasonably leverage multi-frame information for enhancement.

WRQE

Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

FractalNet

FractalNet: Ultra-Deep Neural Networks without Residuals

**CRISS**, or **Cross-lingual Retrievial for Iterative Self-Supervised Training (CRISS)**, is a self-supervised learning method for multilingual sequence generation. CRISS is developed based on the finding that the encoder outputs of multilingual denoising autoencoder can be used as language agnostic representation to retrieve parallel sentence pairs, and training the model on these retrieved sentence pairs can further improve its sentence retrieval and translation capabilities in an iterative manner. Using only unlabeled data from many different languages, CRISS iteratively mines for parallel sentences across languages, trains a new better multilingual model using these mined sentence pairs, mines again for better parallel sentences, and repeats.

Source	FractalNet: Ultra-Deep Neural Networks without Residuals
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com