**NT-ASGD**, or **Non-monotonically Triggered ASGD**, is an averaged stochastic gradient descent technique. 

In regular ASGD, we take steps identical to [regular SGD](https://paperswithcode.com/method/sgd) but instead of returning the last iterate as the solution, we return $\frac{1}{\left(K-T+1\right)}\sum^{T}\_{i=T}w\_{i}$, where $K$ is the total number of iterations and $T < K$ is a user-specified averaging trigger.

NT-ASGD has a non-monotonic criterion that conservatively triggers the averaging when the validation metric fails to improve for multiple cycles. Given that the choice of triggering is irreversible, this conservatism ensures that the randomness of training does not play a major role in the decision.

R-Mix (Random Mix-up) is a Mix-up family Data Augmentation method. It combines random Mix-up with Saliency-guided mix-up, producing a procedure that is fast and performant, while reserving good characteristics of Saliency-guided Mix-up such as low Expected Calibration Error and high Weakly-supervised Object Localization accuracy.

R-Mix

Expeditious Saliency-guided Mix-up through Random Gradient Thresholding

NT-ASGD

Regularizing and Optimizing LSTM Language Models

**Guided Anchoring** is an anchoring scheme for object detection which leverages semantic features to guide the anchoring. The method is motivated by the observation that objects are not distributed evenly over the image. The scale of an object is also closely related to the imagery content, its location and geometry of the scene. Following this intuition, the method generates sparse anchors in two steps: first identifying sub-regions that may contain objects and then determining the shapes at different locations.

Source	Regularizing and Optimizing LSTM Language Models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com