**RetinaNet-RS** is an object detection model produced through a model scaling method based on changing the the input resolution and [ResNet](https://paperswithcode.com/method/resnet) backbone depth. For [RetinaNet](https://paperswithcode.com/method/retinanet), we scale up input resolution from 512 to 768 and the ResNet backbone depth from 50 to 152. As RetinaNet performs dense one-stage object detection, the authors find scaling up input resolution leads to large resolution feature maps hence more anchors to process. This results in a higher capacity dense prediction heads and expensive NMS. Scaling stops at input resolution 768 × 768 for RetinaNet.

**MixText** is a semi-supervised learning method for text classification, which uses a new data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. The technique leverages advances in data augmentation to guess low-entropy labels for unlabeled data, making them as easy to use as labeled data.

MixText

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

RetinaNet-RS

Simple Training Strategies and Model Scaling for Object Detection

Class of methods in Bayesian Statistics where the posterior distribution is approximated over a rejection scheme on simulations because the likelihood function is intractable.

Different parameters get sampled and simulated. Then a distance function is calculated to measure the quality of the simulation compared to data from real observations. Only simulations that fall below a certain threshold get accepted.

Image source: [Kulkarni et al.](https://www.umass.edu/nanofabrics/sites/default/files/PDF_0.pdf)

Source	Simple Training Strategies and Model Scaling for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com