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Found 7 papers, 2 papers with code
Conformal Prediction in Multi-User Settings: An Evaluation
Although those strategies are better suited for multi-user systems, they are typically assessed with respect to performance metrics that capture the overall behavior of the models and do not provide any performance guarantees for individual predictions nor they provide any feedback about the predictions' uncertainty.
An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams
In safety-critical domains such as autonomous driving and medical diagnosis, the reliability of machine learning models is crucial.
Long Range Object-Level Monocular Depth Estimation for UAVs
Computer vision-based object detection is a key modality for advanced Detect-And-Avoid systems that allow for autonomous flight missions of UAVs.
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss
Convolutional Neural Networks (CNNs) are nowadays often employed in vision-based perception stacks for safetycritical applications such as autonomous driving or Unmanned Aerial Vehicles (UAVs).
On The Reliability Of Machine Learning Applications In Manufacturing Environments
The increasing deployment of advanced digital technologies such as Internet of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial environments is enabling the productive use of machine learning (ML) algorithms in the manufacturing domain.
Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time
In this paper we propose a novel 3D single-shot object detection method for detecting vehicles in monocular RGB images.
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
In addition, we complement the Cityscapes benchmark suite with 3D vehicle detection based on the new annotations as well as metrics presented in this work.
