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).

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.

CW-ERM

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

Fast-OCR

Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

**NoisyNet-DQN** is a modification of a [DQN](https://paperswithcode.com/method/dqn) that utilises noisy linear layers for exploration instead of $\epsilon$-greedy exploration as in the original DQN formulation.

Source	Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com