2022
DOI: 10.48550/arxiv.2202.03868
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Mapping DNN Embedding Manifolds for Network Generalization Prediction

Abstract: Understanding Deep Neural Network (DNN) performance in changing conditions is essential for deploying DNNs in safety critical applications with unconstrained environments, e.g., perception for self-driving vehicles or medical image analysis. Recently, the task of Network Generalization Prediction (NGP) has been proposed to predict how a DNN will generalize in a new operating domain. Previous NGP approaches have relied on labeled metadata and known distributions for the new operating domains. In this study, we … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 17 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?