Please enter a description about the method here

As CNN features are naturally spatial, channel-wise and multi-layer, 
Chen et al. proposed a novel spatial and channel-wise attention-based convolutional neural network (SCA-CNN). 
It was designed for the task of image captioning, and uses an encoder-decoder framework where a CNN first encodes an input image into a vector and then an LSTM decodes the vector into a sequence of words. Given an input feature map $X$ and the previous time step LSTM hidden state $h_{t-1} \in \mathbb{R}^d$, a spatial attention mechanism pays more attention to the semantically useful regions, guided by LSTM hidden state $h_{t-1}$. The  spatial attention model is:

\begin{align}
a(h_{t-1}, X) &= \tanh(Conv_1^{1 \times 1}(X) \oplus W_1 h_{t-1})
\end{align}

\begin{align}
\Phi_s(h_{t-1}, X) &= \text{Softmax}(Conv_2^{1 \times 1}(a(h_{t-1}, X)))    
\end{align}

where $\oplus$ represents  addition of a matrix and a vector. Similarly, channel-wise attention aggregates global information first, and then computes a channel-wise attention weight vector with the hidden state $h_{t-1}$:
\begin{align}
b(h_{t-1}, X) &= \tanh((W_2\text{GAP}(X)+b_2)\oplus W_1h_{t-1})
\end{align}
\begin{align}
\Phi_c(h_{t-1}, X) &= \text{Softmax}(W_3(b(h_{t-1}, X))+b_3)    
\end{align}
Overall, the  SCA mechanism can be written in one of two ways. If channel-wise attention is applied before spatial attention, we have
\begin{align}
Y &= f(X,\Phi_s(h_{t-1}, X \Phi_c(h_{t-1}, X)), \Phi_c(h_{t-1}, X)) 
\end{align}
and  if spatial attention comes first:
\begin{align}
Y &= f(X,\Phi_s(h_{t-1}, X), \Phi_c(h_{t-1}, X \Phi_s(h_{t-1}, X)))
\end{align}
where $f(\cdot)$ denotes the modulate function which takes the feature map $X$ and attention maps as input and then outputs the modulated feature map $Y$.

Unlike previous attention mechanisms which consider each image region equally and use global spatial information to tell the network where to focus, SCA-Net leverages the semantic vector to produce the spatial attention map as well as the channel-wise attention weight vector. Being more than a powerful attention model, SCA-CNN also provides a better understanding of where and what the model should focus on during sentence generation.

SCA-CNN

SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning

Spectral Dropout

Regularization of Deep Neural Networks with Spectral Dropout

Anisotropic convolution is a central building block of CNNs but challenging to transfer to surfaces. DeltaConv learns combinations and compositions of operators from vector calculus, which are a natural fit for curved surfaces. The result is a simple and robust anisotropic convolution operator for point clouds with state-of-the-art results.

Source	Regularization of Deep Neural Networks with Spectral Dropout
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com