Add to ORKGMany studies have recently been published on recognizing when a classification neural network is provided with data that does not fit into one of the class labels learnt during training. These so-called out-of-distribution (OOD) detection approaches have the potential to improve system safety in situations when unexpected or new inputs might cause mistakes that jeopardize human life. These approaches would particularly be able to aid autonomous vehicles if they could be used to detect and pinpoint anomalous objects in a driving environment, allowing the system to either fail gracefully or to treat such objects with extreme caution. We dive deep into an existing and promising OOD Detection method from the image classification literature ca...
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee ...
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee ...
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generali...
The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as aut...
The work presented in this thesis addresses the problem of Out-of-Distribution (OOD) detection in de...
The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as aut...
The field of Out-of-Distribution (OOD) detection aims to separate OOD data from in-distribution (ID)...
We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural netw...
Out-of-distribution (OOD) detection can be used in deep learning-based applications to reject outlie...
International audienceThe usage of deep neural networks in safety-critical systems is limited by our...
Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data...
International audienceThe usage of deep neural networks in safety-critical systems is limited by our...
Modern neural networks can assign high confidence to inputs drawn from outside the training distribu...
Deep Neural Networks (DNNs) are extensively deployed in today’s safety-critical autonomous sy...
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee ...
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee ...
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee ...
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generali...
The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as aut...
The work presented in this thesis addresses the problem of Out-of-Distribution (OOD) detection in de...
The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as aut...
The field of Out-of-Distribution (OOD) detection aims to separate OOD data from in-distribution (ID)...
We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural netw...
Out-of-distribution (OOD) detection can be used in deep learning-based applications to reject outlie...
International audienceThe usage of deep neural networks in safety-critical systems is limited by our...
Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data...
International audienceThe usage of deep neural networks in safety-critical systems is limited by our...
Modern neural networks can assign high confidence to inputs drawn from outside the training distribu...
Deep Neural Networks (DNNs) are extensively deployed in today’s safety-critical autonomous sy...
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee ...
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee ...
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee ...
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generali...