A closed-loop evaluation procedure is first used in a simulator to identify training data samples that are important for practical driving performance and then we these samples to help debias the policy network.

A **Spatial Attention Module** is a module for spatial attention in convolutional neural networks. It generates a spatial attention map by utilizing the inter-spatial relationship of features. Different from the [channel attention](https://paperswithcode.com/method/channel-attention-module), the spatial attention focuses on where is an informative part, which is complementary to the channel attention. To compute the spatial attention, we first apply average-pooling and max-pooling operations along the channel axis and concatenate them to generate an efficient feature descriptor. On the concatenated feature descriptor, we apply a [convolution](https://paperswithcode.com/method/convolution) layer to generate a spatial attention map $\textbf{M}\_{s}\left(F\right) \in \mathcal{R}^{H×W}$ which encodes where to emphasize or suppress. 

We aggregate channel information of a feature map by using two pooling operations, generating two 2D maps: $\mathbf{F}^{s}\_{avg} \in \mathbb{R}^{1\times{H}\times{W}}$ and $\mathbf{F}^{s}\_{max} \in \mathbb{R}^{1\times{H}\times{W}}$. Each denotes average-pooled features and max-pooled features across the channel. Those are then concatenated and convolved by a standard convolution layer, producing the 2D spatial attention map. In short, the spatial attention is computed as:

$$ \textbf{M}\_{s}\left(F\right) = \sigma\left(f^{7x7}\left(\left[\text{AvgPool}\left(F\right);\text{MaxPool}\left(F\right)\right]\right)\right) $$

$$ \textbf{M}\_{s}\left(F\right) = \sigma\left(f^{7x7}\left(\left[\mathbf{F}^{s}\_{avg};\mathbf{F}^{s}\_{max} \right]\right)\right) $$

where $\sigma$ denotes the sigmoid function and $f^{7×7}$ represents a convolution operation with the filter size of 7 × 7.

Spatial Attention Module

CBAM: Convolutional Block Attention Module

CW-ERM

CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization

Fast-OCR is a new lightweight detection network that incorporates features from existing models focused on the speed/accuracy trade-off, such as [YOLOv2](https://paperswithcode.com/method/yolov2), [CR-NET](https://paperswithcode.com/method/cr-net), and Fast-[YOLOv4](https://paperswithcode.com/method/yolov4).

Source	CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com