**MaxUp** is an adversarial data augmentation technique for improving the generalization performance of machine learning models. The idea is to generate a set of augmented data with some random perturbations or transforms, and minimize the maximum, or worst case loss over the augmented data.  By doing so, we implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance.  For example, in the case of Gaussian perturbation, MaxUp is asymptotically equivalent to using the gradient norm of the loss as a penalty to encourage smoothness.

**BASNet**, or **Boundary-Aware Segmentation Network**, is an image segmentation architecture that consists of a predict-refine architecture and a hybrid loss. The proposed BASNet comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation.  The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual 
 refinement module, which are respectively used to predict and refine a segmentation probability map. The hybrid loss is a combination of the binary cross entropy, structural similarity and intersection-over-union losses, which guide the network to learn three-level (i.e., pixel-, patch- and map- level) hierarchy representations.

BASNet

Boundary-Aware Segmentation Network for Mobile and Web Applications

MaxUp

MaxUp: A Simple Way to Improve Generalization of Neural Network Training

**DIoU-NMS** is a type of non-maximum suppression where we use Distance IoU rather than regular DIoU, in which the overlap area and the distance between two central points of bounding boxes are simultaneously considered when suppressing redundant boxes.

In original NMS, the IoU metric is used to suppress the redundant detection boxes, where the overlap area is the unique factor, often yielding false suppression for the cases with occlusion. With DIoU-NMS, we not only consider the overlap area but also central point distance between two boxes.

Source	MaxUp: A Simple Way to Improve Generalization of Neural Network Training
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com