- Home
- Search Results
- Page 1 of 1
Search for: All records
-
Total Resources4
- Resource Type
-
00000040000
- More
- Availability
-
40
- Author / Contributor
- Filter by Author / Creator
-
-
Batmanghelich, Kayhan (4)
-
Xu, Yanwu (4)
-
Chen, Junxiang (3)
-
Gong, Mingming (3)
-
Bakas, Spyridon (1)
-
Belkov, Arseniy (1)
-
Calisto, Maria Baldeon (1)
-
Cardoso, Jorge (1)
-
Chen, Ziye (1)
-
Choi, Jae Won (1)
-
Dawant, Benoit M. (1)
-
Dong, Hexin (1)
-
Dorent, Reuben (1)
-
Escalera, Sergio (1)
-
Fan, Yubo (1)
-
Glocker, Ben (1)
-
Hansen, Lasse (1)
-
Heinrich, Mattias P. (1)
-
Ivory, Marina (1)
-
Joshi, Smriti (1)
-
- Filter by Editor
-
-
& Spizer, S. M. (0)
-
& . Spizer, S. (0)
-
& Ahn, J. (0)
-
& Bateiha, S. (0)
-
& Bosch, N. (0)
-
& Brennan K. (0)
-
& Brennan, K. (0)
-
& Chen, B. (0)
-
& Chen, Bodong (0)
-
& Drown, S. (0)
-
& Ferretti, F. (0)
-
& Higgins, A. (0)
-
& J. Peters (0)
-
& Kali, Y. (0)
-
& Ruiz-Arias, P.M. (0)
-
& S. Spitzer (0)
-
& Sahin. I. (0)
-
& Spitzer, S. (0)
-
& Spitzer, S.M. (0)
-
(submitted - in Review for IEEE ICASSP-2024) (0)
-
-
Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Dorent, Reuben ; Kujawa, Aaron ; Ivory, Marina ; Bakas, Spyridon ; Rieke, Nicola ; Joutard, Samuel ; Glocker, Ben ; Cardoso, Jorge ; Modat, Marc ; Batmanghelich, Kayhan ; et al ( , Medical Image Analysis)
-
Xu, Yanwu ; Gong, Mingming ; Chen, Junxiang ; Liu, Tongliang ; Zhang, Kun ; Batmanghelich, Kayhan ( , Proceedings of the AAAI Conference on Artificial Intelligence)The majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generative-discriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and is able to generate high-quality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarily-labeled data.more » « less
-
Xu, Yanwu ; Gong, Mingming ; Chen, Junxiang ; Chen, Ziye ; Batmanghelich, Kayhan ( , Frontiers in Neuroscience)