**Meta Pseudo Labels** is a semi-supervised learning method that uses a teacher network to generate pseudo labels on unlabeled data to teach a student network. The teacher receives feedback from the student to inform the teacher to generate better pseudo labels. This feedback signal is used as a reward to train the teacher throughout the course of the student’s learning.

**Disp R-CNN** is a 3D object detection system for stereo images. It utilizes an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, a statistical shape model is used to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds.

Disp R-CNN

Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

Meta Pseudo Labels

**Random Scaling** is a type of image data augmentation where we randomly change the scale the image between a specified range.

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