**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.

**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.

FractalNet

FractalNet: Ultra-Deep Neural Networks without Residuals

CRISS

Cross-lingual Retrieval for Iterative Self-Supervised Training

**Patch AutoAugment** is a patch-level automatic data augmentation algorithm that automatically searches for the optimal augmentation policies for the patches of an image. Specifically, PAA allows each patch DA operation to be controlled by an agent and models it as a Multi-Agent Reinforcement Learning (MARL) problem. At each step, PAA samples the most effective operation for each patch based on its content and the semantics of the whole image. The agents cooperate as a team and share a unified team reward for achieving the joint optimal DA policy of the whole image. PAA is co-trained with a target network through adversarial training. At each step, the policy network samples the most effective operation for each patch based on its content and the semantics of the image.

Source	Cross-lingual Retrieval for Iterative Self-Supervised Training
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com