Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ryan Benkert"'
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-12
In recent years, deep neural networks have significantly impacted the seismic interpretation process. Due to the simple implementation and low interpretation costs, deep neural networks are an attractive component for the common interpretation pipeli
This paper considers deep out-of-distribution active learning. In practice, fully trained neural networks interact randomly with out-of-distribution (OOD) inputs and map aberrant samples randomly within the model representation space. Since data repr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30099adf32b5d0861d07e96aaa3994a3
http://arxiv.org/abs/2301.05106
http://arxiv.org/abs/2301.05106
Even though deep neural networks have shown tremendous success in countless applications, explaining model behaviour or predictions is an open research problem. In this paper, we address this issue by employing a simple yet effective method by analys
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa9dad848bd2a7d6c7213614c639ca0b
http://arxiv.org/abs/2301.04221
http://arxiv.org/abs/2301.04221
In active learning, acquisition functions define informativeness directly on the representation position within the model manifold. However, for most machine learning models (in particular neural networks) this representation is not fixed due to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5146eb100a59cb644df00dba36598d1
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Publikováno v:
Second International Meeting for Applied Geoscience & Energy.
Publikováno v:
First International Meeting for Applied Geoscience & Energy Expanded Abstracts.