**Conditional Random Fields** or **CRFs** are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is modeled as a graphical model, which implements dependencies between the predictions. Graph choice depends on the application, for example linear chain CRFs are popular in natural language processing, whereas in image-based tasks, the graph would connect to neighboring locations in an image to enforce that they have similar predictions.

Image Credit: [Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields](https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf)

**SCARLET-NAS** is a type of [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) that utilises a learnable stabilizer to calibrate feature deviation, named the Equivariant Learnable Stabilizer (ELS). Previous one-shot approaches can be limited by fixed-depth search spaces. With SCARLET-NAS, we use the equivariant learnable stabilizer on each skip connection. This can lead to improved convergence, more reliable evaluation, and retained equivalence. The third benefit is deemed most important by the authors for scalability.

SCARLET-NAS

SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search

**Feature Non-Maximum Suppression**, or **FeatureNMS**, is a post-processing step for object detection models that removes duplicates where there are multiple detections outputted per object. FeatureNMS recognizes duplicates not only based on the intersection over union between the bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com