**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.

**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.

NT-ASGD

Regularizing and Optimizing LSTM Language Models

Guided Anchoring

Region Proposal by Guided Anchoring

**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) layers.

Source	Region Proposal by Guided Anchoring
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com