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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
Attentive Normalization
Attentive NormalizationEuropean Conference on Computer Vision (ECCV), 2019
Xilai Li
Wei Sun
Tianfu Wu
OODViT
221
35
0
04 Aug 2019
Sound source detection, localization and classification using
  consecutive ensemble of CRNN models
Sound source detection, localization and classification using consecutive ensemble of CRNN modelsWorkshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019
Slawomir Kapka
M. Lewandowski
263
72
0
02 Aug 2019
AutoML: A Survey of the State-of-the-Art
AutoML: A Survey of the State-of-the-ArtKnowledge-Based Systems (KBS), 2019
Xin He
Kaiyong Zhao
Xiaowen Chu
787
1,687
0
02 Aug 2019
An Empirical Study of Batch Normalization and Group Normalization in
  Conditional Computation
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Vincent Michalski
Vikram S. Voleti
Samira Ebrahimi Kahou
Anthony Ortiz
Pascal Vincent
C. Pal
Doina Precup
BDL
233
7
0
31 Jul 2019
Efficient Method for Categorize Animals in the Wild
Efficient Method for Categorize Animals in the Wild
Abulikemu Abuduweili
Xin Wu
Xingchen Tao
90
5
0
30 Jul 2019
KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks
KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks
A. Tiulpin
Iaroslav Melekhov
S. Saarakkala
198
51
0
29 Jul 2019
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Charting the Right Manifold: Manifold Mixup for Few-shot LearningIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2019
Puneet Mangla
M. Singh
Abhishek Sinha
Nupur Kumari
V. Balasubramanian
Balaji Krishnamurthy
SSL
406
366
0
28 Jul 2019
Deep Learning for Classification and Severity Estimation of Coffee Leaf
  Biotic Stress
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic StressComputers and Electronics in Agriculture (Comput. Electron. Agric.), 2019
José G. M. Esgario
R. Krohling
J. A. Ventura
118
274
0
26 Jul 2019
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from
  Retinal Images
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal ImagesInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019
Fei Yu
Jie Zhao
Y. Gong
Zhi Wang
Yuxi Li
Fan Yang
Bin Dong
Shijie Zhao
Li Zhang
GANMedIm
159
35
0
26 Jul 2019
Overfitting of neural nets under class imbalance: Analysis and
  improvements for segmentation
Overfitting of neural nets under class imbalance: Analysis and improvements for segmentationInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019
Zeju Li
Konstantinos Kamnitsas
Ben Glocker
SSeg
115
102
0
25 Jul 2019
Defense Against Adversarial Attacks Using Feature Scattering-based
  Adversarial Training
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial TrainingNeural Information Processing Systems (NeurIPS), 2019
Haichao Zhang
Jianyu Wang
AAML
375
242
0
24 Jul 2019
HODGEPODGE: Sound event detection based on ensemble of semi-supervised
  learning methods
HODGEPODGE: Sound event detection based on ensemble of semi-supervised learning methodsWorkshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019
Ziqiang Shi
Liu Liu
Huibin Lin
Rujie Liu
Anyan Shi
133
20
0
17 Jul 2019
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk
  Minimization
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk MinimizationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Kenji Kawaguchi
Haihao Lu
ODL
537
74
0
09 Jul 2019
Improving short text classification through global augmentation methods
Improving short text classification through global augmentation methodsInternational Cross-Domain Conference on Machine Learning and Knowledge Extraction (CD-MAKE), 2019
Vukosi Marivate
T. Sefara
VLM
109
107
0
07 Jul 2019
Adversarial Attacks in Sound Event Classification
Adversarial Attacks in Sound Event Classification
Vinod Subramanian
Emmanouil Benetos
N. Xu
SKoT McDonald
Mark Sandler
AAML
147
11
0
04 Jul 2019
Attention based Convolutional Recurrent Neural Network for Environmental
  Sound Classification
Attention based Convolutional Recurrent Neural Network for Environmental Sound ClassificationChinese Conference on Pattern Recognition and Computer Vision (CPRCV), 2019
Zhichao Zhang
Shugong Xu
Tianhao Qiao
Shunqing Zhang
Shan Cao
136
128
0
04 Jul 2019
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural
  Architecture Search
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture SearchIEEE International Conference on Computer Vision (ICCV), 2019
Xiangxiang Chu
Bo Zhang
Ruijun Xu
385
355
0
03 Jul 2019
The Receptive Field as a Regularizer in Deep Convolutional Neural
  Networks for Acoustic Scene Classification
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene ClassificationEuropean Signal Processing Conference (EUSIPCO), 2019
Khaled Koutini
Hamid Eghbalzadeh
Matthias Dorfer
Gerhard Widmer
98
92
0
03 Jul 2019
Learning Data Augmentation Strategies for Object Detection
Learning Data Augmentation Strategies for Object DetectionEuropean Conference on Computer Vision (ECCV), 2019
Barret Zoph
E. D. Cubuk
Golnaz Ghiasi
Nayeon Lee
Jonathon Shlens
Quoc V. Le
196
580
0
26 Jun 2019
Improving performance of deep learning models with axiomatic attribution
  priors and expected gradients
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
G. Erion
Joseph D. Janizek
Pascal Sturmfels
Scott M. Lundberg
Su-In Lee
OODBDLFAtt
290
84
0
25 Jun 2019
Exploring Self-Supervised Regularization for Supervised and
  Semi-Supervised Learning
Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning
Phi Vu Tran
SSL
175
17
0
25 Jun 2019
Mixup of Feature Maps in a Hidden Layer for Training of Convolutional
  Neural Network
Mixup of Feature Maps in a Hidden Layer for Training of Convolutional Neural NetworkInternational Conference on Neural Information Processing (ICONIP), 2018
Hideki Oki
Takio Kurita
SSL
75
4
0
24 Jun 2019
Defending Against Adversarial Examples with K-Nearest Neighbor
Chawin Sitawarin
David Wagner
AAML
210
29
0
23 Jun 2019
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised
  Classification with the 1-Laplacian Graph Energy
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
Angelica I. Aviles-Rivero
Nicolas Papadakis
Ruoteng Li
P. Sellars
Samar M. Alsaleh
R. Tan
Carola-Bibiane Schönlieb
320
3
0
20 Jun 2019
Adversarial Learning for Improved Onsets and Frames Music Transcription
Adversarial Learning for Improved Onsets and Frames Music TranscriptionInternational Society for Music Information Retrieval Conference (ISMIR), 2019
Jong Wook Kim
J. P. Bello
266
52
0
20 Jun 2019
Efficient data augmentation using graph imputation neural networks
Efficient data augmentation using graph imputation neural networksIntelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2019
Indro Spinelli
Simone Scardapane
M. Scarpiniti
A. Uncini
70
5
0
20 Jun 2019
Data Interpolating Prediction: Alternative Interpretation of Mixup
Data Interpolating Prediction: Alternative Interpretation of Mixup
Takuya Shimada
Shoichiro Yamaguchi
K. Hayashi
Sosuke Kobayashi
143
7
0
20 Jun 2019
Prune and Replace NAS
Prune and Replace NASInternational Conference on Machine Learning and Applications (ICMLA), 2019
Kevin Laube
A. Zell
178
13
0
18 Jun 2019
MixUp as Directional Adversarial Training
MixUp as Directional Adversarial Training
Guillaume P. Archambault
Yongyi Mao
Ziqiao Wang
Richong Zhang
AAML
126
23
0
17 Jun 2019
Interpolated Adversarial Training: Achieving Robust Neural Networks
  without Sacrificing Too Much Accuracy
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
Alex Lamb
Vikas Verma
Kenji Kawaguchi
Alexander Matyasko
Savya Khosla
Arno Solin
Yoshua Bengio
AAML
454
106
0
16 Jun 2019
Fixing the train-test resolution discrepancy
Fixing the train-test resolution discrepancyNeural Information Processing Systems (NeurIPS), 2019
Hugo Touvron
Andrea Vedaldi
Matthijs Douze
Edouard Grave
455
464
0
14 Jun 2019
Synthesizing Diverse Lung Nodules Wherever Massively: 3D
  Multi-Conditional GAN-based CT Image Augmentation for Object Detection
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object DetectionInternational Conference on 3D Vision (3DV), 2019
Changhee Han
Yoshiro Kitamura
Akira Kudo
Akimichi Ichinose
L. Rundo
Yujiro Furukawa
Kazuki Umemoto
Yuanzhong Li
Hideki Nakayama
MedIm
210
130
0
12 Jun 2019
Suppressing Model Overfitting for Image Super-Resolution Networks
Suppressing Model Overfitting for Image Super-Resolution Networks
Ruicheng Feng
Jinjin Gu
Yu Qiao
Chao Dong
SupR
166
43
0
11 Jun 2019
Online Object Representations with Contrastive Learning
Online Object Representations with Contrastive Learning
Soren Pirk
Mohi Khansari
Yunfei Bai
Corey Lynch
P. Sermanet
SSL
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0
10 Jun 2019
Improved Adversarial Robustness via Logit Regularization Methods
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Cecilia Summers
M. Dinneen
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10 Jun 2019
A Preliminary Study on Data Augmentation of Deep Learning for Image
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Benlin Hu
Cheng-Hsun Lei
Dong Wang
Shu Zhang
Zhenyu Chen
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Improving Robustness Without Sacrificing Accuracy with Patch Gaussian
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Raphael Gontijo-Lopes
Dong Yin
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Justin Gilmer
E. D. Cubuk
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06 Jun 2019
How to Initialize your Network? Robust Initialization for WeightNorm &
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How to Initialize your Network? Robust Initialization for WeightNorm & ResNetsNeural Information Processing Systems (NeurIPS), 2019
Devansh Arpit
Victor Campos
Yoshua Bengio
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Semi-supervised semantic segmentation needs strong, varied perturbations
Semi-supervised semantic segmentation needs strong, varied perturbations
Geoff French
S. Laine
Timo Aila
Michal Mackiewicz
G. Finlayson
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AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI
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AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI SegmentationInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019
Pierrick Coupé
Boris Mansencal
Michael Clement
Rémi Giraud
B. D. D. Senneville
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Achieving Generalizable Robustness of Deep Neural Networks by Stability
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Wojciech Samek
Nils Strodthoff
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Deeply-supervised Knowledge Synergy
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Data Augmentation for Object Detection via Progressive and Selective
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Fan Yang
Yongzi Yu
W. Zuo
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The Principle of Unchanged Optimality in Reinforcement Learning
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Xingyou Song
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Time Matters in Regularizing Deep Networks: Weight Decay and Data
  Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near ConvergenceNeural Information Processing Systems (NeurIPS), 2019
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Alessandro Achille
Stefano Soatto
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Meta Dropout: Learning to Perturb Features for Generalization
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Taewook Nam
Eunho Yang
Sung Ju Hwang
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158
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Probabilistic Decoupling of Labels in Classification
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Jeppe Nørregaard
Lars Kai Hansen
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89
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High Frequency Component Helps Explain the Generalization of
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High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksComputer Vision and Pattern Recognition (CVPR), 2019
Haohan Wang
Xindi Wu
Pengcheng Yin
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Image Deformation Meta-Networks for One-Shot Learning
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Yanwei Fu
Yu-Xiong Wang
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Derivative Manipulation for General Example Weighting
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