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Observations on K-image Expansion of Image-Mixing Augmentation for
  Classification

Observations on K-image Expansion of Image-Mixing Augmentation for Classification

8 October 2021
Joonhyun Jeong
Sungmin Cha
Jongwon Choi
Sangdoo Yun
Taesup Moon
Y. Yoo
    VLM
ArXivPDFHTML

Papers citing "Observations on K-image Expansion of Image-Mixing Augmentation for Classification"

8 / 8 papers shown
Title
A Survey of Mix-based Data Augmentation: Taxonomy, Methods,
  Applications, and Explainability
A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
Chengtai Cao
Fan Zhou
Yurou Dai
Jianping Wang
Kunpeng Zhang
AAML
24
28
0
21 Dec 2022
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function
  Perspective
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
Chanwoo Park
Sangdoo Yun
Sanghyuk Chun
AAML
21
32
0
21 Aug 2022
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Jang-Hyun Kim
Wonho Choo
Hosan Jeong
Hyun Oh Song
197
176
0
05 Feb 2021
Re-labeling ImageNet: from Single to Multi-Labels, from Global to
  Localized Labels
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
Sangdoo Yun
Seong Joon Oh
Byeongho Heo
Dongyoon Han
Junsuk Choe
Sanghyuk Chun
403
142
0
13 Jan 2021
Aggregated Residual Transformations for Deep Neural Networks
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie
Ross B. Girshick
Piotr Dollár
Z. Tu
Kaiming He
297
10,220
0
16 Nov 2016
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
271
5,329
0
05 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
284
2,889
0
15 Sep 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,138
0
06 Jun 2015
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