** 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.

Triplet attention comprises of three branches each responsible for capturing crossdimension between the spatial dimensions and channel dimension of the input. Given an input tensor with shape (C × H × W), each branch is responsible for aggregating cross-dimensional interactive features between either the spatial dimension H or W and the channel dimension C.

Triplet Attention

Rotate to Attend: Convolutional Triplet Attention Module

SiLU

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

**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.

Source	Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com