FCANet contains a novel multi-spectral channel attention module. Given an input feature map $X \in \mathbb{R}^{C \times H \times W}$, multi-spectral channel attention first splits $X$ into many parts $x^{i} \in \mathbb{R}^{C' \times H \times W}$. Then it applies a 2D DCT to each part $x^{i}$. Note that a 2D DCT can use pre-processing results to reduce computation. After processing each part,  all results are concatenated into a vector. Finally, fully connected layers, ReLU activation and a sigmoid are used to get the attention vector as in an SE block. This can be formulated as:
\begin{align}
    s = F_\text{fca}(X, \theta) & = \sigma (W_{2} \delta (W_{1}[(\text{DCT}(\text{Group}(X)))]))
\end{align}
\begin{align}
    Y & = s  X
\end{align}
where $\text{Group}(\cdot)$ indicates dividing the input into many groups and $\text{DCT}(\cdot)$ is the 2D discrete cosine transform. 

This work based on information compression and discrete cosine transforms achieves excellent performance on the classification task.

Class of methods in Bayesian Statistics where the posterior distribution is approximated over a rejection scheme on simulations because the likelihood function is intractable.

Different parameters get sampled and simulated. Then a distance function is calculated to measure the quality of the simulation compared to data from real observations. Only simulations that fall below a certain threshold get accepted.

Image source: [Kulkarni et al.](https://www.umass.edu/nanofabrics/sites/default/files/PDF_0.pdf)

Accelerating Simulation-based Inference with Emerging AI Hardware

FcaNet

FcaNet: Frequency Channel Attention Networks

**Criss-Cross Network** (**CCNet**) aims to obtain full-image contextual information in an effective and efficient way. Concretely,
for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. **CCNet** is with the following
merits: **1)** GPU memory friendly. Compared with the [non-local block](https://paperswithcode.com/method/non-local-block), the proposed recurrent criss-cross attention module requires 11× less GPU memory usage. **2)** High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block. **3)** The state-of-the-art performance.

Source	FcaNet: Frequency Channel Attention Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com