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Found 15 papers, 4 papers with code
Out-of-Distribution Detection for LiDAR-based 3D Object Detection
Detecting OOD inputs is challenging and essential for the safe deployment of models.
A Safety Assurable Human-Inspired Perception Architecture
While research in addressing these limitations is active, in this paper, we argue that a fundamentally different approach is needed to address them.
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components
In this paper, we address the need for defining machine-verifiable reliability requirements for MVCs against transformations that simulate the full range of realistic and safety-critical changes in the environment.
The missing link: Developing a safety case for perception components in automated driving
In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components.
The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches
In addition to comparing several OODD approaches using our proposed robustness score, we demonstrate that some optimization methods provide better solutions for OODD approaches.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation
The detection of out of distribution samples for image classification has been widely researched.
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
Out-of-distribution Detection in Classifiers via Generation
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Improving Confident-Classifiers For Out-of-distribution Detection
In the context of OOD detection for image classification, one of the recent approaches proposes training a classifier called “confident-classifier” by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KLdivergence between the predictive distribution of OOD samples in the low-density“boundary” of in-distribution and the uniform distribution (maximizing the entropy of the outputs).
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Analysis of Confident-Classifiers for Out-of-distribution Detection
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution).
Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving
Perception is a safety-critical function of autonomous vehicles and machine learning (ML) plays a key role in its implementation.
Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance
There is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems.
Calibrating Uncertainties in Object Localization Task
In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making.
Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262
In automotive development, safety is a critical objective, and the emergence of standards such as ISO 26262 has helped focus industry practices to address safety in a systematic and consistent way.
An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software
We then provide a set of recommendations on how to adapt the standard to accommodate ML.
