**Exact Fusion Model (EFM)** is a method for aggregating a feature pyramid. The EFM is based on [YOLOv3](https://paperswithcode.com/method/yolov3), which assigns exactly one bounding-box prior to each ground truth object. Each ground truth bounding box corresponds to one anchor box that surpasses the threshold IoU. If the size of an anchor box is equivalent to the field-of-view of the grid cell, then for the grid cells of the $s$-th scale, the corresponding bounding box will be lower bounded by the $(s − 1)$th scale and upper bounded by the (s + 1)th scale. Therefore, the EFM assembles features from the three scales.

**ACKTR**, or **Actor Critic with Kronecker-factored Trust Region**, is an actor-critic method for reinforcement learning that applies [trust region optimization](https://paperswithcode.com/method/trpo) using a recently proposed Kronecker-factored approximation to the curvature. The method extends the framework of natural policy gradient and optimizes both the actor and the critic using Kronecker-factored approximate
curvature (K-FAC) with trust region.

ACTKR

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

Exact Fusion Model

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

**LapStyle**, or **Laplacian Pyramid Network**, is a feed-forward style transfer method. It uses a [Drafting Network](https://paperswithcode.com/method/drafting-network) to transfer global style patterns in low-resolution, and adopts higher resolution [Revision Networks](https://paperswithcode.com/method/revision-network) to revise local styles in a pyramid manner according to outputs of multi-level Laplacian filtering of the content image. Higher resolution details can be generated by stacking Revision Networks with multiple Laplacian pyramid levels. The final stylized image is obtained by aggregating outputs of all pyramid levels.

Specifically, we first generate image pyramid $\left$\bar{x}\_{c}, r\_{c}\right$$ from content image $x\_{c}$ with the help of Laplacian filter. Rough low-resolution stylized image are then generated by the Drafting Network. Then the Revision Network generates stylized detail image in high resolution. Then the final stylized image is generated by aggregating the outputs pyramid. $L, C$ and $A$ in an image represent Laplacian, concatenate and aggregation operation separately.

Source	CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com