**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.

** Sigmoid Linear Units**, or **SiLUs**, are activation functions for
neural networks. The activation of the SiLU is computed by the sigmoid function multiplied by its input, or $$ x\sigma(x).$$

See [Gaussian Error Linear Units](https://arxiv.org/abs/1606.08415) ([GELUs](https://paperswithcode.com/method/gelu)) where the SiLU was originally coined, and see [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://arxiv.org/abs/1702.03118) and [Swish: a Self-Gated Activation Function](https://arxiv.org/abs/1710.05941v1) where the SiLU was experimented with later.

SiLU

Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning

FreeAnchor

FreeAnchor: Learning to Match Anchors for Visual Object Detection

**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)

Source	FreeAnchor: Learning to Match Anchors for Visual Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com