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SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
v1v2 (latest)

SapAugment: Learning A Sample Adaptive Policy for Data Augmentation

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020
2 November 2020
Ting-Yao Hu
A. Shrivastava
Jen-Hao Rick Chang
H. Koppula
Stefan Braun
Kyuyeon Hwang
Ozlem Kalinli
Oncel Tuzel
ArXiv (abs)PDFHTML

Papers citing "SapAugment: Learning A Sample Adaptive Policy for Data Augmentation"

11 / 11 papers shown
Complexity boosted adaptive training for better low resource ASR
  performance
Complexity boosted adaptive training for better low resource ASR performance
Hongxuan Lu
Shenjian Wang
Biao Li
301
0
0
01 Dec 2024
Automated data processing and feature engineering for deep learning and
  big data applications: a survey
Automated data processing and feature engineering for deep learning and big data applications: a surveyJournal of Information and Intelligence (JII), 2024
A. Mumuni
F. Mumuni
TPM
373
152
0
18 Mar 2024
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methodsKnowledge and Information Systems (KAIS), 2024
A. Mumuni
F. Mumuni
479
24
0
13 Mar 2024
Towards Automatic Data Augmentation for Disordered Speech Recognition
Towards Automatic Data Augmentation for Disordered Speech RecognitionIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023
Zengrui Jin
Xurong Xie
Tianzi Wang
Mengzhe Geng
Jiajun Deng
Guinan Li
Shujie Hu
Xunying Liu
241
9
0
14 Dec 2023
G-Augment: Searching for the Meta-Structure of Data Augmentation
  Policies for ASR
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASRSpoken Language Technology Workshop (SLT), 2022
Gary Wang
Ekin D.Cubuk
Andrew Rosenberg
Shuyang Cheng
Ron J. Weiss
Bhuvana Ramabhadran
Pedro J. Moreno
Quoc V. Le
Daniel S. Park
302
5
0
19 Oct 2022
A Policy-based Approach to the SpecAugment Method for Low Resource E2E
  ASR
A Policy-based Approach to the SpecAugment Method for Low Resource E2E ASRAsia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2022
Rui Li
Guodong Ma
Dexin Zhao
Ranran Zeng
Xiaoyu Li
Haolin Huang
177
6
0
16 Oct 2022
FedEmbed: Personalized Private Federated Learning
FedEmbed: Personalized Private Federated Learning
Andrew Silva
Katherine Metcalf
N. Apostoloff
B. Theobald
FedML
205
7
0
18 Feb 2022
Dynamic Data Augmentation with Gating Networks for Time Series
  Recognition
Dynamic Data Augmentation with Gating Networks for Time Series RecognitionInternational Conference on Pattern Recognition (ICPR), 2021
Daisuke Oba
Shinnosuke Matsuo
Brian Kenji Iwana
AI4TS
286
1
0
05 Nov 2021
SpliceOut: A Simple and Efficient Audio Augmentation Method
SpliceOut: A Simple and Efficient Audio Augmentation Method
Arjit Jain
Pranay Reddy Samala
Deepak Mittal
Preethi Jyothi
M. Singh
555
14
0
30 Sep 2021
DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for
  Embedded Speech and Audio Processing from Decentralised Data
DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data
Shahin Amiriparian
Tobias Hübner
Maurice Gerczuk
Sandra Ottl
Björn W. Schuller
199
3
0
23 Apr 2021
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASRInterspeech (Interspeech), 2021
Tsz Kin Lam
Mayumi Ohta
Shigehiko Schamoni
Stefan Riezler
353
27
0
03 Apr 2021
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