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Deep Representation Learning with an Information-theoretic Loss
v1v2v3v4v5 (latest)

Deep Representation Learning with an Information-theoretic Loss

25 November 2021
S. Ando
    DRL
ArXiv (abs)PDFHTML

Papers citing "Deep Representation Learning with an Information-theoretic Loss"

5 / 5 papers shown
Title
Generalized Out-of-Distribution Detection: A Survey
Generalized Out-of-Distribution Detection: A Survey
Jingkang Yang
Kaiyang Zhou
Yixuan Li
Ziwei Liu
284
927
0
21 Oct 2021
Multi-Class Data Description for Out-of-distribution Detection
Multi-Class Data Description for Out-of-distribution Detection
Dongha Lee
Sehun Yu
Hwanjo Yu
OODD
36
37
0
02 Apr 2021
Unsupervised Anomaly Detection with Generative Adversarial Networks to
  Guide Marker Discovery
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
T. Schlegl
Philipp Seeböck
S. Waldstein
U. Schmidt-Erfurth
Georg Langs
MedImGAN
108
2,230
0
17 Mar 2017
Deep Variational Information Bottleneck
Deep Variational Information Bottleneck
Alexander A. Alemi
Ian S. Fischer
Joshua V. Dillon
Kevin Patrick Murphy
126
1,721
0
01 Dec 2016
Deep Learning and the Information Bottleneck Principle
Deep Learning and the Information Bottleneck Principle
Naftali Tishby
Noga Zaslavsky
DRL
210
1,584
0
09 Mar 2015
1