Search Results for author:
Found 4 papers, 2 papers with code
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation
To address this issue, we build upon the recent ObsNet approach by providing object instance knowledge to the observer.
Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation
In this paper, we tackle the detection of out-of-distribution (OOD) objects in semantic segmentation.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving
In this paper, we show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving, by triggering a fallback behavior if a target accuracy cannot be guaranteed.
Leveraging Model Interpretability and Stability to increase Model Robustness
We then use an error detector in the form of a binary classifier on top of the DNN to automatically discriminate wrong and correct predictions of the DNN based on their hidden unit activations.
