**Displaced Aggregation Unit** replaces classic [convolution](https://paperswithcode.com/method/convolution) layer in ConvNets with learnable positions of units.  This introduces explicit structure of hierarchical compositions and results in several benefits:

* fully adjustable and **learnable receptive fields** through spatially-adjustable filter units
* **reduced parameters** for spatial coverage
efficient inference
* **decupling** of the parameters from the receptive field sizes

More information can be found [here.](https://www.vicos.si/Research/DeepCompositionalNet)

**FreeAnchor** is an anchor supervision method for object detection. Many CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In contrast, FreeAnchor is a learning-to-match approach that breaks the IoU restriction, allowing objects to match anchors in a flexible manner. It updates hand-crafted anchor assignment to free anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization.

FreeAnchor

FreeAnchor: Learning to Match Anchors for Visual Object Detection

DAU-ConvNet

Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks

The extremely low computational cost of lightweight CNNs constrains the depth and width of the networks, further decreasing their representational power. To address the above problem, Chen et al. proposed dynamic convolution, a novel operator design that increases  representational power with negligible additional computational cost and does not change the width or depth of the network in parallel with CondConv.

Dynamic convolution uses $K$ parallel convolution kernels of the same  size and input/output dimensions instead of one kernel per layer. Like SE blocks, it adopts a squeeze-and-excitation mechanism to generate the attention weights for the different convolution kernels. These kernels are then aggregated dynamically by weighted summation and applied to the input feature map $X$:
\begin{align}
    s & = \text{softmax} (W_{2} \delta (W_{1}\text{GAP}(X)))
\end{align}
\begin{align}
    \text{DyConv} &= \sum_{i=1}^{K} s_k \text{Conv}_k 
\end{align}
\begin{align}
    Y &= \text{DyConv}(X)
\end{align}
Here the convolutions are combined by summation of weights and biases of convolutional kernels. 

Compared to applying convolution to the feature map, the computational cost of squeeze-and-excitation and weighted summation is extremely low. Dynamic convolution thus provides an efficient operation to improve  representational power and can be easily used as a replacement for any convolution.

Source	Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com