**DAFNe** is a dense one-stage anchor-free deep model for oriented object detection. It is a deep neural network that performs predictions on a dense grid over the input image, being architecturally simpler in design, as well as easier to optimize than its two-stage counterparts. Furthermore, it reduces the prediction complexity by refraining from employing bounding box anchors. This enables a tighter fit to oriented objects, leading to a better separation of bounding boxes especially in case of dense object distributions. Moreover, it introduces an orientation-aware generalization of the center-ness function to arbitrary quadrilaterals that takes into account the object's orientation and that, accordingly, accurately down-weights low-quality predictions

**Label Quality Model** is an intermediate supervised task aimed at predicting the clean labels from noisy labels by leveraging rater features and a paired subset for supervision. The LQM technique assumes the existence of rater features and a subset of training data with both noisy and clean labels, which we call paired-subset. In real world scenarios, some level of label noise may be unavoidable. The LQM approach still works as long as the clean(er) label is less noisy than a label from a rater that is randomly selected from the pool, e.g., clean labels can be from either expert raters or aggregation of multiple raters. LQM is trained on the paired-subset using rater features and noisy label as input, and inferred on the entire training corpus. The output of LQM is used during model training as a more accurate alternative to the noisy labels.

Label Quality Model

An Instance-Dependent Simulation Framework for Learning with Label Noise

DAFNe

DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection

**Gradient Checkpointing** is a method used for reducing the memory footprint when training deep neural networks, at the cost of having a small increase in computation time.

Source	DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com