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Wakeword Detection under Distribution Shifts

Wakeword Detection under Distribution Shifts

13 July 2022
S. Parthasarathi
Lu Zeng
Christin Jose
Joe Wang
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Papers citing "Wakeword Detection under Distribution Shifts"

14 / 14 papers shown
Title
On Front-end Gain Invariant Modeling for Wake Word Spotting
On Front-end Gain Invariant Modeling for Wake Word Spotting
Yixin Gao
Noah D. Stein
Chieh-Chi Kao
Yunliang Cai
Ming Sun
Tao Zhang
S. Vitaladevuni
24
11
0
13 Oct 2020
Accurate Detection of Wake Word Start and End Using a CNN
Accurate Detection of Wake Word Start and End Using a CNN
Christin Jose
Yuriy Mishchenko
Thibaud Sénéchal
Anish Shah
Alex Escott
S. Vitaladevuni
34
26
0
09 Aug 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx)
Out-of-Distribution Generalization via Risk Extrapolation (REx)
David M. Krueger
Ethan Caballero
J. Jacobsen
Amy Zhang
Jonathan Binas
Dinghuai Zhang
Rémi Le Priol
Aaron Courville
OOD
298
931
0
02 Mar 2020
Invariant Risk Minimization
Invariant Risk Minimization
Martín Arjovsky
Léon Bottou
Ishaan Gulrajani
David Lopez-Paz
OOD
177
2,222
0
05 Jul 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot
Nicholas Carlini
Ian Goodfellow
Nicolas Papernot
Avital Oliver
Colin Raffel
140
3,024
0
06 May 2019
Lessons from Building Acoustic Models with a Million Hours of Speech
Lessons from Building Acoustic Models with a Million Hours of Speech
S. Parthasarathi
N. Strom
73
88
0
02 Apr 2019
Do ImageNet Classifiers Generalize to ImageNet?
Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht
Rebecca Roelofs
Ludwig Schmidt
Vaishaal Shankar
OOD
SSeg
VLM
109
1,714
0
13 Feb 2019
ImageNet-trained CNNs are biased towards texture; increasing shape bias
  improves accuracy and robustness
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos
Patricia Rubisch
Claudio Michaelis
Matthias Bethge
Felix Wichmann
Wieland Brendel
96
2,666
0
29 Nov 2018
Out-of-Distribution Detection using Multiple Semantic Label
  Representations
Out-of-Distribution Detection using Multiple Semantic Label Representations
Gabi Shalev
Yossi Adi
Joseph Keshet
OODD
76
85
0
20 Aug 2018
Strong Baselines for Neural Semi-supervised Learning under Domain Shift
Strong Baselines for Neural Semi-supervised Learning under Domain Shift
Sebastian Ruder
Barbara Plank
36
172
0
25 Apr 2018
Continual Lifelong Learning with Neural Networks: A Review
Continual Lifelong Learning with Neural Networks: A Review
G. I. Parisi
Ronald Kemker
Jose L. Part
Christopher Kanan
S. Wermter
KELM
CLL
172
2,883
0
21 Feb 2018
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
328
4,624
0
10 Nov 2016
Regularization With Stochastic Transformations and Perturbations for
  Deep Semi-Supervised Learning
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
Mehdi S. M. Sajjadi
Mehran Javanmardi
Tolga Tasdizen
BDL
80
1,111
0
14 Jun 2016
Learning with Pseudo-Ensembles
Learning with Pseudo-Ensembles
Philip Bachman
O. Alsharif
Doina Precup
70
598
0
16 Dec 2014
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