**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.

**Adaptive Dropout** is a regularization technique that extends dropout by allowing the dropout probability to be different for different units. The intuition is that there may be hidden units that can individually make confident predictions for the presence or absence of an important feature or combination of features. [Dropout](https://paperswithcode.com/method/dropout) will ignore this confidence and drop the unit out 50% of the time. 

Denote the activity of unit $j$ in a deep neural network by $a\_{j}$ and assume that its inputs are {$a\_{i}: i < j$}. In dropout, $a\_{j}$ is randomly set to zero with probability 0.5. Let $m\_{j}$ be a binary variable that is used to mask, the activity $a\_{j}$, so that its value is:

$$ a\_{j} = m\_{j}g \left( \sum\_{i: i<j}w\_{j, i}a\_{i} \right)$$

where $w\_{j,i}$ is the weight from unit $i$ to unit $j$ and $g\left(·\right)$ is the activation function and $a\_{0} = 1$ accounts for biases. Whereas in standard dropout, $m\_{j}$ is Bernoulli with probability $0.5$, adaptive dropout uses adaptive dropout probabilities that depends on input activities:

$$ P\left(m\_{j} = 1\mid{\{a\_{i}: i < j\}}\right) = f \left( \sum\_{i: i<j}\pi{\_{j, i}a\_{i}} \right) $$

where $\pi\_{j, i}$ is the weight from unit $i$ to unit $j$ in the standout network or the adaptive dropout network; $f(·)$ is a sigmoidal function. Here 'standout' refers to a binary belief network is that is overlaid on a neural network as part of the overall regularization technique.

Adaptive Dropout

Adaptive dropout for training deep neural networks

ACTKR

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

**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.

Source	Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com