2020

ResizeMix: Mixing Data with Preserved Object Information and True Labels

Qin, Jie, Fang, Jiemin, Zhang, Qian et al.

Understand

Data augmentation is a powerful technique to increase the diversity of data, which can effectively improve the generalization ability of neural networks in image recognition tasks.

Recent data mixing based augmentation strategies have achieved great success.
Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping a patch from one image and pasting it on another image.
To further promote the performance of CutMix, a series of works explore to use the saliency information of the image to guide the mixing.

Built on

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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Imagenet large scale visual recognition challenge
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QSGD: randomized quantization for communication-optimal stochastic gradient descent
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Incorporating nesterov momentum into adam
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Deep residual learning for image recognition
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Deep residual learning for image recognition
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Similar

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Shake-shake regularization
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
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Autoaugment: Learning augmentation policies from data
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Dropblock: A regularization method for convolutional networks
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Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation
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Then

Population based augmentation: Efficient learning of augmentation policy schedules
Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, and Pieter Abbeel · 2019
Later among the works it cites.
Fast autoaugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
Later among the works it cites.
Online hyper-parameter learning for auto-augmentation strategy
Chen Lin, Minghao Guo, Chuming Li, Xin Yuan, Wei Wu, Junjie Yan, Dahua Lin, and Wanli Ouyang · 2019
Later among the works it cites.
DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Later among the works it cites.
Improved mixed-example data augmentation
Cecilia Summers and Michael J Dinneen · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Later among the works it cites.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Closest in time.
Supermix: Supervising the mixing data augmentation
Original
Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, and Nasser M Nasrabadi · 2020
Closest in time.
Fast neural network adaptation via parameter remapping and architecture search
Jiemin Fang, Yuzhu Sun, Kangjian Peng, Qian Zhang, Yuan Li, Wenyu Liu, and Xinggang Wang · 2020
Closest in time.
Densely connected search space for more flexible neural architecture search
Jiemin Fang, Yuzhu Sun, Qian Zhang, Yuan Li, Wenyu Liu, and Xinggang Wang · 2020
Closest in time.
Fna++: Fast network adaptation via parameter remapping and architecture search
Jiemin Fang, Yuzhu Sun, Qian Zhang, Kangjian Peng, Yuan Li, Wenyu Liu, and Xinggang Wang · 2020
Closest in time.
Fmix: Enhancing mixed sample data augmentation
Original
Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, and Adam Prügel-Bennett Jonathon Hare · 2020
Closest in time.
Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
Closest in time.
Augment your batch: better training with larger batches
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry · 2020
Closest in time.
Puzzle mix: Exploiting saliency and local statistics for optimal mixup
Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song · 2020
Closest in time.
Dada: Differentiable automatic data augmentation
Original
Yonggang Li, Guosheng Hu, Yongtao Wang, Timothy Hospedales, Neil M Robertson, and Yongxing Yang · 2020
Closest in time.
Saliencymix: A saliency guided data augmentation strategy for better regularization
Original
AFM Uddin, Mst Monira, Wheemyung Shin, TaeChoong Chung, Sung-Ho Bae, et al · 2020
Closest in time.
Adversarial autoaugment
Xinyu Zhang, Qiang Wang, Jian Zhang, and Zhao Zhong · 2020
Closest in time.
Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
Closest in time.

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