Rohde & Schwarz: Deep feature representation with auxiliary embedding
Thesis by: Christoph BaurAdvisor: Prof. Nassir Navab
Supervision by: Shadi Albarqouni, Athanasios Karamalis
Abstract
While convolutional neural networks (CNNs) show outstanding results in various computer vision tasks, the deep feature representations within these models often lack transparency and discriminative power. By introducing additional regularization to the networks, one hopes to find more robust and discriminative embeddings of the feature representations such that for instance different classes cluster nicely. In fact, previous work successfully employed regularization techniques under supervised and semi-supervised settings and reported improved robustness and better generalization on well known computer vision datasets. The work in this master thesis investigates how different regularization techniques for auxiliary manifold embedding affect the performance of the models - as opposed to previous work - for challenging medical data. Further, the work compares the results using both supervised and semi-supervised training.Location
Rohde & Schwarz GmbH? & Co. KG. Berg am Laim Mühldorfstraße 15 81671 München, DeutschlandResultant Paper
| 2017 | |
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C. Baur, S. Albarqouni, N. Navab
Semi-Supervised Learning for Fully Convolutional Networks Accepted to Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Quebec, Canada, September 2017 A pre-print version is available online at arXiv. The first two authors contribute equally to this paper. (bib) |
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| Title: | Rohde & Schwarz: Deep feature representation with auxiliary embedding |
| Abstract: | While convolutional neural networks (CNNs) show outstanding results in various computer vision tasks, the deep feature representations within these models often lack transparency and discriminative power. By introducing additional regularization to the networks, one hopes to find more robust and discriminative embeddings of the feature representations such that for instance different classes cluster nicely. In fact, previous work successfully employed regularization techniques under supervised and semi-supervised settings and reported improved robustness and better generalization on well known computer vision datasets. The work in this master thesis investigates how different regularization techniques for auxiliary manifold embedding affect the performance of the models - as opposed to previous work - for challenging medical data. Further, the work compares the results using both supervised and semi-supervised training. |
| Student: | Christoph Baur |
| Director: | Prof. Nassir Navab |
| Supervisor: | Shadi Albarqouni, Athanasios Karamalis |
| Type: | Master Thesis |
| Area: | Machine Learning |
| Status: | finished |
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