ECANet

2000년 도입 · 논문 5편에서 사용

An ECA block has similar formulation to an SE block including a squeeze module for aggregating global spatial information and an efficient excitation module for modeling cross-channel interaction. Instead of indirect correspondence, an ECA block only considers direct interaction between each channel and its k-nearest neighbors to control model complexity. Overall, the formulation of an ECA block is: \begin{align} s = F_\text{eca}(X, \theta) & = \sigma (\text{Conv1D}(\text{GAP}(X))) \end{align} \begin{align} Y & = s X \end{align} where $\text{Conv1D}(\cdot)$ denotes 1D convolution with a kernel of shape $k$ across the channel domain, to model local cross-channel interaction. The parameter $k$ decides the coverage of interaction, and in ECA the kernel size $k$ is adaptively determined from the channel dimensionality $C$ instead of by manual tuning, using cross-validation: \begin{equation} k = \psi(C) = \left | \frac{\log_2(C)}{\gamma}+\frac{b}{\gamma}\right |_\text{odd} \end{equation} where $\gamma$ and $b$ are hyperparameters. $|x|_\text{odd}$ indicates the nearest odd function of $x$. Compared to SENet, ECANet has an improved excitation module, and provides an efficient and effective block which can readily be incorporated into various CNNs.

출처: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

소개 논문: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks