**Sample Consistency Network (SCNet)** is a method for instance segmentation which ensures the IoU distribution of the samples at training time are as close to that at inference time. To this end, only the outputs of the last box stage are used for mask predictions at both training and inference. The Figure shows the IoU distribution of the samples going to the mask branch at training time with/without sample consistency compared to that at inference time.

**Circular Smooth Label** (CSL) is a classification-based rotation detection technique for arbitrary-oriented object detection. It is used for circularly distributed angle classification and addresses the periodicity of the angle and increases the error tolerance to adjacent angles.

On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited

SCNet

SCNet: Training Inference Sample Consistency for Instance Segmentation

PipeDream is an asynchronous pipeline parallel strategy for training large neural networks. It adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible.

Source	SCNet: Training Inference Sample Consistency for Instance Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com