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mixup: Beyond Empirical Risk Minimization
v1v2 (latest)

mixup: Beyond Empirical Risk Minimization

International Conference on Learning Representations (ICLR), 2017
25 October 2017
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
    NoLa
ArXiv (abs)PDFHTML

Papers citing "mixup: Beyond Empirical Risk Minimization"

50 / 5,332 papers shown
On Mixup Training: Improved Calibration and Predictive Uncertainty for
  Deep Neural Networks
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural NetworksNeural Information Processing Systems (NeurIPS), 2019
S. Thulasidasan
Gopinath Chennupati
J. Bilmes
Tanmoy Bhattacharya
S. Michalak
UQCV
552
588
0
27 May 2019
Blockwise Adaptivity: Faster Training and Better Generalization in Deep
  Learning
Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning
Shuai Zheng
James T. Kwok
ODL
167
5
0
23 May 2019
Multi-Sample Dropout for Accelerated Training and Better Generalization
Multi-Sample Dropout for Accelerated Training and Better Generalization
H. Inoue
169
78
0
23 May 2019
Augmenting Data with Mixup for Sentence Classification: An Empirical
  Study
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Ziqiao Wang
Yongyi Mao
Richong Zhang
193
258
0
22 May 2019
Semi-Supervised Learning by Augmented Distribution Alignment
Semi-Supervised Learning by Augmented Distribution AlignmentIEEE International Conference on Computer Vision (ICCV), 2019
Qin Wang
Wen Li
Luc Van Gool
260
75
0
20 May 2019
DARC: Differentiable ARchitecture Compression
DARC: Differentiable ARchitecture Compression
Shashank Singh
A. Khetan
Zohar Karnin
AI4CE
135
8
0
20 May 2019
Online Hyper-parameter Learning for Auto-Augmentation Strategy
Online Hyper-parameter Learning for Auto-Augmentation StrategyIEEE International Conference on Computer Vision (ICCV), 2019
Chen Lin
Minghao Guo
Chuming Li
Yuan Xin
Wei Wu
Dahua Lin
Wanli Ouyang
Junjie Yan
ODL
130
90
0
17 May 2019
CutMix: Regularization Strategy to Train Strong Classifiers with
  Localizable Features
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesIEEE International Conference on Computer Vision (ICCV), 2019
Sangdoo Yun
Dongyoon Han
Seong Joon Oh
Sanghyuk Chun
Junsuk Choe
Y. Yoo
OOD
1.6K
5,566
0
13 May 2019
Multi-class Novelty Detection Using Mix-up Technique
Multi-class Novelty Detection Using Mix-up TechniqueIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2019
Supritam Bhattacharjee
Devraj Mandal
Soma Biswas
228
14
0
11 May 2019
Virtual Mixup Training for Unsupervised Domain Adaptation
Virtual Mixup Training for Unsupervised Domain Adaptation
Xudong Mao
Yun Ma
Zhenguo Yang
Yangbin Chen
Qing Li
344
53
0
10 May 2019
EENA: Efficient Evolution of Neural Architecture
EENA: Efficient Evolution of Neural Architecture
Hui Zhu
Zhulin An
Chuanguang Yang
Kaiqiang Xu
Erhu Zhao
Yongjun Xu
3DV
250
43
0
10 May 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning
MixMatch: A Holistic Approach to Semi-Supervised LearningNeural Information Processing Systems (NeurIPS), 2019
David Berthelot
Nicholas Carlini
Ian Goodfellow
Nicolas Papernot
Avital Oliver
Colin Raffel
555
3,381
0
06 May 2019
Privacy-Preserving Deep Neural Networks with Pixel-based Image
  Encryption Considering Data Augmentation in the Encrypted Domain
Privacy-Preserving Deep Neural Networks with Pixel-based Image Encryption Considering Data Augmentation in the Encrypted DomainInternational Conference on Information Photonics (ICIP), 2019
Warit Sirichotedumrong
Takahiro Maekawa
Yuma Kinoshita
Hitoshi Kiya
154
92
0
06 May 2019
A Survey on Neural Architecture Search
A Survey on Neural Architecture Search
Martin Wistuba
Ambrish Rawat
Tejaswini Pedapati
AI4CE
249
281
0
04 May 2019
Billion-scale semi-supervised learning for image classification
Billion-scale semi-supervised learning for image classification
I. Z. Yalniz
Edouard Grave
Kan Chen
Manohar Paluri
D. Mahajan
SSL
370
479
0
02 May 2019
Fast AutoAugment
Fast AutoAugmentNeural Information Processing Systems (NeurIPS), 2019
Sungbin Lim
Ildoo Kim
Taesup Kim
Chiheon Kim
Sungwoong Kim
354
645
0
01 May 2019
Introducing Graph Smoothness Loss for Training Deep Learning
  Architectures
Introducing Graph Smoothness Loss for Training Deep Learning ArchitecturesData Science Workshop (DS), 2019
Myriam Bontonou
Carlos Lassance
G. B. Hacene
Vincent Gripon
Jian Tang
Antonio Ortega
143
19
0
01 May 2019
Unsupervised Data Augmentation for Consistency Training
Unsupervised Data Augmentation for Consistency TrainingNeural Information Processing Systems (NeurIPS), 2019
Qizhe Xie
Zihang Dai
Eduard H. Hovy
Minh-Thang Luong
Quoc V. Le
846
2,553
0
29 Apr 2019
Unsupervised Label Noise Modeling and Loss Correction
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo Sanchez
Diego Ortego
Paul Albert
Noel E. O'Connor
Kevin McGuinness
NoLa
413
694
0
25 Apr 2019
Inner-Imaging Networks: Put Lenses into Convolutional Structure
Inner-Imaging Networks: Put Lenses into Convolutional Structure
Yang Hu
Guihua Wen
Mingnan Luo
Dan Dai
Wenming Cao
Zhiwen Yu
Wendy Hall
231
3
0
22 Apr 2019
Stochastic Region Pooling: Make Attention More Expressive
Stochastic Region Pooling: Make Attention More Expressive
Mingnan Luo
Guihua Wen
Yang Hu
Dan Dai
Yingxue Xu
110
8
0
22 Apr 2019
Good-Enough Compositional Data Augmentation
Good-Enough Compositional Data Augmentation
Jacob Andreas
384
240
0
21 Apr 2019
End-to-End Robotic Reinforcement Learning without Reward Engineering
End-to-End Robotic Reinforcement Learning without Reward Engineering
Avi Singh
Larry Yang
Kristian Hartikainen
Chelsea Finn
Sergey Levine
SSLOffRL
322
279
0
16 Apr 2019
Unsupervised Singing Voice Conversion
Unsupervised Singing Voice Conversion
Eliya Nachmani
Lior Wolf
280
58
0
13 Apr 2019
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural
  Networks with Octave Convolution
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
Yunpeng Chen
Haoqi Fan
Bing Xu
Zhicheng Yan
Yannis Kalantidis
Marcus Rohrbach
Shuicheng Yan
Jiashi Feng
295
637
0
10 Apr 2019
CondConv: Conditionally Parameterized Convolutions for Efficient
  Inference
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Brandon Yang
Gabriel Bender
Quoc V. Le
Jiquan Ngiam
MedIm3DV
399
760
0
10 Apr 2019
Exploring Uncertainty Measures for Image-Caption Embedding-and-Retrieval
  Task
Exploring Uncertainty Measures for Image-Caption Embedding-and-Retrieval Task
Kenta Hama
Takashi Matsubara
K. Uehara
Jianfei Cai
BDLUQCV
171
6
0
09 Apr 2019
Few-Shot Learning via Saliency-guided Hallucination of Samples
Few-Shot Learning via Saliency-guided Hallucination of Samples
Hongguang Zhang
Jing Zhang
Piotr Koniusz
198
212
0
06 Apr 2019
A Comprehensive Overhaul of Feature Distillation
A Comprehensive Overhaul of Feature Distillation
Byeongho Heo
Jeesoo Kim
Sangdoo Yun
Hyojin Park
Nojun Kwak
J. Choi
372
685
0
03 Apr 2019
Exploiting Synthetically Generated Data with Semi-Supervised Learning
  for Small and Imbalanced Datasets
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
Maria Perez-Ortiz
Peter Tiño
Rafał K. Mantiuk
C. Hervás‐Martínez
87
17
0
24 Mar 2019
Convolution with even-sized kernels and symmetric padding
Convolution with even-sized kernels and symmetric paddingNeural Information Processing Systems (NeurIPS), 2019
Shuang Wu
Guanrui Wang
Pei Tang
F. Chen
Luping Shi
219
77
0
20 Mar 2019
Manifold Mixup improves text recognition with CTC loss
Manifold Mixup improves text recognition with CTC loss
Bastien Moysset
Ronaldo O. Messina
119
4
0
11 Mar 2019
Interpolation Consistency Training for Semi-Supervised Learning
Interpolation Consistency Training for Semi-Supervised Learning
Vikas Verma
Kenji Kawaguchi
Alex Lamb
Arno Solin
Arno Solin
Yoshua Bengio
David Lopez-Paz
336
868
0
09 Mar 2019
On Adversarial Mixup Resynthesis
On Adversarial Mixup Resynthesis
Christopher Beckham
S. Honari
Vikas Verma
Alex Lamb
F. Ghadiri
R. Devon Hjelm
Yoshua Bengio
C. Pal
AAML
316
12
0
07 Mar 2019
Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Jiangchao Yao
Ya Zhang
Ivor W. Tsang
Jun-wei Sun
NoLa
129
1
0
06 Mar 2019
Complement Objective Training
Complement Objective TrainingInternational Conference on Learning Representations (ICLR), 2019
Hao-Yun Chen
Pei-Hsin Wang
Chun-Hao Liu
Shih-Chieh Chang
Jia Pan
Yutian Chen
Wei Wei
Da-Cheng Juan
AAML
194
53
0
04 Mar 2019
SPDA: Superpixel-based Data Augmentation for Biomedical Image
  Segmentation
SPDA: Superpixel-based Data Augmentation for Biomedical Image SegmentationInternational Conference on Medical Imaging with Deep Learning (MIDL), 2018
Yizhe Zhang
Lin Yang
Hao Zheng
Peixian Liang
Colleen A. Mangold
R. Loreto
David P. Hughes
Benlin Liu
MedIm
134
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28 Feb 2019
Single-frame Regularization for Temporally Stable CNNs
Single-frame Regularization for Temporally Stable CNNsComputer Vision and Pattern Recognition (CVPR), 2019
Gabriel Eilertsen
Rafał K. Mantiuk
Jonas Unger
196
46
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27 Feb 2019
LaSO: Label-Set Operations networks for multi-label few-shot learning
LaSO: Label-Set Operations networks for multi-label few-shot learningComputer Vision and Pattern Recognition (CVPR), 2019
Amit Alfassy
Leonid Karlinsky
Amit Aides
J. Shtok
Sivan Harary
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MultiGrain: a unified image embedding for classes and instances
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Bag of Freebies for Training Object Detection Neural Networks
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Tong He
Hang Zhang
Zhongyue Zhang
Junyuan Xie
Mu Li
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Semi-Supervised and Task-Driven Data Augmentation
Semi-Supervised and Task-Driven Data AugmentationInformation Processing in Medical Imaging (IPMI), 2019
K. Chaitanya
Neerav Karani
Christian F. Baumgartner
O. Donati
Anton S. Becker
E. Konukoglu
MedIm
327
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Bidirectional Inference Networks: A Class of Deep Bayesian Networks for
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Chengzhi Mao
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Mingmin Zhao
Tommi Jaakkola
Dina Katabi
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An Empirical Study on Regularization of Deep Neural Networks by Local
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Jiahui Yu
Xingjian Li
Jun Huan
Thomas S. Huang
AI4CE
249
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03 Feb 2019
Augment your batch: better training with larger batches
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Elad Hoffer
Tal Ben-Nun
Itay Hubara
Niv Giladi
Torsten Hoefler
Daniel Soudry
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226
78
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Fixup Initialization: Residual Learning Without Normalization
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Hongyi Zhang
Yann N. Dauphin
Tengyu Ma
ODLAI4CE
325
371
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PadChest: A large chest x-ray image dataset with multi-label annotated
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A. Pertusa
J. M. Salinas
M. Iglesia-Vayá
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309
715
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Differentially Private ADMM for Distributed Medical Machine Learning
Differentially Private ADMM for Distributed Medical Machine Learning
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Xiaoqi Qin
Wenjun Xu
Yanmin Gong
Zhu Han
Miao Pan
FedML
452
20
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Chinese Herbal Recognition based on Competitive Attentional Fusion of
  Multi-hierarchies Pyramid Features
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Yingxue Xu
Guihua Wen
Yang Hu
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Dan Dai
Y. Zhuang
MedIm
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DLOW: Domain Flow for Adaptation and Generalization
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