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Learning with Out-of-Distribution Data for Audio Classification

Learning with Out-of-Distribution Data for Audio Classification

11 February 2020
Turab Iqbal
Yin Cao
Qiuqiang Kong
Mark D. Plumbley
Wenwu Wang
    OODD
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Papers citing "Learning with Out-of-Distribution Data for Audio Classification"

16 / 16 papers shown
Title
SpecAugment: A Simple Data Augmentation Method for Automatic Speech
  Recognition
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Daniel S. Park
William Chan
Yu Zhang
Chung-Cheng Chiu
Barret Zoph
E. D. Cubuk
Quoc V. Le
VLM
152
3,435
0
18 Apr 2019
Learning Sound Event Classifiers from Web Audio with Noisy Labels
Learning Sound Event Classifiers from Web Audio with Noisy Labels
Eduardo Fonseca
Manoj Plakal
D. Ellis
F. Font
Xavier Favory
Xavier Serra
NoLa
55
110
0
04 Jan 2019
MixUp as Locally Linear Out-Of-Manifold Regularization
MixUp as Locally Linear Out-Of-Manifold Regularization
Hongyu Guo
Yongyi Mao
Richong Zhang
48
321
0
07 Sep 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with
  Noisy Labels
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhilu Zhang
M. Sabuncu
NoLa
60
2,580
0
20 May 2018
Predictive Uncertainty Estimation via Prior Networks
Predictive Uncertainty Estimation via Prior Networks
A. Malinin
Mark Gales
UD
BDL
EDL
UQCV
PER
168
907
0
28 Feb 2018
Learning Confidence for Out-of-Distribution Detection in Neural Networks
Learning Confidence for Out-of-Distribution Detection in Neural Networks
Terrance Devries
Graham W. Taylor
OOD
OODD
70
584
0
13 Feb 2018
mixup: Beyond Empirical Risk Minimization
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
251
9,687
0
25 Oct 2017
Enhancing The Reliability of Out-of-distribution Image Detection in
  Neural Networks
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang
Yixuan Li
R. Srikant
UQCV
OODD
100
2,054
0
08 Jun 2017
Learning from Noisy Labels with Distillation
Learning from Noisy Labels with Distillation
Yuncheng Li
Jianchao Yang
Yale Song
Liangliang Cao
Jiebo Luo
Li Li
NoLa
69
549
0
07 Mar 2017
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
  in Neural Networks
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks
Kevin Gimpel
UQCV
103
3,420
0
07 Oct 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN
3DV
639
36,599
0
25 Aug 2016
Distilling the Knowledge in a Neural Network
Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton
Oriol Vinyals
J. Dean
FedML
246
19,523
0
09 Mar 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
889
149,474
0
22 Dec 2014
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Scott E. Reed
Honglak Lee
Dragomir Anguelov
Christian Szegedy
D. Erhan
Andrew Rabinovich
NoLa
104
1,014
0
20 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
963
99,991
0
04 Sep 2014
Training Convolutional Networks with Noisy Labels
Training Convolutional Networks with Noisy Labels
Sainbayar Sukhbaatar
Joan Bruna
Manohar Paluri
Lubomir D. Bourdev
Rob Fergus
NoLa
78
270
0
09 Jun 2014
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