**DeepCluster** is a self-supervision approach for learning image representations.  DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update
the weights of the network

The **Cross-Attention** module is an attention module used in [CrossViT](https://paperswithcode.com/method/crossvit) for fusion of multi-scale features. The CLS token of the large branch (circle) serves as a query token to interact with the patch tokens from the small branch through attention. $f\left(·\right)$ and $g\left(·\right)$ are projections to align dimensions. The small branch follows the same procedure but swaps CLS and patch tokens from another branch.

Cross-Attention Module

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

DeepCluster

Deep Clustering for Unsupervised Learning of Visual Features

**Meta-augmentation** helps generate more varied tasks for a single example in meta-learning. It can be distinguished from data augmentation in classic machine learning as follows. For data augmentation in classical machine learning, the aim is to generate more varied examples, within a single task. Meta-augmentation has the exact opposite aim: we wish to generate more varied tasks,
for a single example, to force the learner to quickly learn a new task from feedback. In meta-augmentation, adding randomness discourages the base learner and model from learning trivial solutions that do not generalize to new tasks.

Source	Deep Clustering for Unsupervised Learning of Visual Features
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com