**DeepViT** is a type of [vision transformer](https://paperswithcode.com/method/vision-transformer) that replaces the self-attention layer within the [transformer](https://paperswithcode.com/method/transformer) block with a [Re-attention module](https://paperswithcode.com/method/re-attention-module) to address the issue of attention collapse and enables training deeper ViTs.

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.

ECANet

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

DeepViT

DeepViT: Towards Deeper Vision Transformer

**Batch-Channel Normalization**, or **BCN**, uses batch knowledge to prevent channel-normalized models from getting too close to "elimination singularities". Elimination singularities correspond to the points on the training trajectory where neurons become consistently deactivated. They cause degenerate manifolds in the loss landscape which will slow down training and harm model performances.

Source	DeepViT: Towards Deeper Vision Transformer
Year	2000
Data Source	Papers with Code archive (CC BY-SA)