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)

**RetinaNet-RS** is an object detection model produced through a model scaling method based on changing the the input resolution and [ResNet](https://paperswithcode.com/method/resnet) backbone depth. For [RetinaNet](https://paperswithcode.com/method/retinanet), we scale up input resolution from 512 to 768 and the ResNet backbone depth from 50 to 152. As RetinaNet performs dense one-stage object detection, the authors find scaling up input resolution leads to large resolution feature maps hence more anchors to process. This results in a higher capacity dense prediction heads and expensive NMS. Scaling stops at input resolution 768 × 768 for RetinaNet.

RetinaNet-RS

Simple Training Strategies and Model Scaling for Object Detection

Accelerating Simulation-based Inference with Emerging AI Hardware

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.

Source	Accelerating Simulation-based Inference with Emerging AI Hardware
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com